SDS PODCAST

EPISODE 121

WITH

ALEX ANTIC

Kirill: This is episode number 121 with Senior Data Scientist at the

Australian Federal Government, Dr Alex Antic.

(background music plays)

Welcome to the SuperDataScience podcast. My name is Kirill

Eremenko, data science coach and lifestyle entrepreneur.

And each week we bring you inspiring people and ideas to

help you build your successful career in data science.

Thanks for being here today and now let’s make the complex

simple.

(background music plays)

Welcome back to the SuperDataScience podcast, ladies and

gentlemen. And today I've got a very interesting and

insightful episode for you. On the show, I have Dr Alex

Antic. Now, Alex started out into the space of data science

with a PhD in Applied Mathematics. And then his career

took him on an incredible whirlwind of journeys. He's been a

quantitative analyst, or a quant, in banks and investment

organisations. He's been in the space of customer analytics.

He's been a consultant at PriceWaterhouseCoopers, and he

has also worked with the Australian Federal Government. So

a very, very diverse background and in the first half of the

podcast, we will walk through all of it and you will find some

very interesting insights and applications that he's seen in

his career. And then in the second half of the podcast - well,

in the second half of the podcast, Alex really surprised us

with some special gifts that he shared on this podcast.

So Alex has a huge wealth of knowledge and experience in

the space of data science, and he actually runs a meetup

group in Canberra for data scientists, and he constantly

helps and mentors other data scientists in this space. And

so Alex was kind enough to actually prepare something for

this podcast. He prepared two guides for data scientists, and

he shared them with us on the podcast. In the second half,

you will find them there. So the first guide is how to become

an effective data scientist. And there, we don't just talk

about technical skills. We talk about technical skills, the

business side of things, communication, and attitude. Well

in fact, Dr Alex just shares all these things, all of his wealth

of knowledge in that space.

And the second guide is for those who want to build a

successful data science practice. So whether you are a

person who wants to get into data science and be the most

effective data scientist that you possibly can, or whether

you're looking to build a successful data science practice, in

both cases you will get incredible value from what Alex

shared on this podcast. In fact, the insights were so amazing

that we couldn't just leave them as audio, and together with

Alex and the design team at SuperDataScience, we put

together two infographics for you. So one for each of those

guides, and you can get those infographics if you go to

www.superdatascience.com/121. You can just download

and keep them in order to help you remember what Alex

mentioned on the podcast, the steps that he outlines,

whether it is for becoming an effective data scientist or

whether it is to build a successful career in data science.

So you have an opportunity, then go and download these

infographics before you listen so you can follow along. If

you're on the go, if you're in the car or you're running or

you're on a bicycle, or you're on public transport, that's ok,

listen to the podcast and then still make sure to download

those infographics so you can keep something tangible that

you can always reference just to refresh on how to do either

of those things.

On that note, you can already hear that I'm very excited

about this podcast, so on that note, without further ado, I

bring to you Dr Alex Antic.

(background music plays)

Welcome ladies and gentlemen to the SuperDataScience

podcast. Today I've got a very special guest calling in from

Australia, Dr Alex Antic. Welcome, Alex, to the show, how

are you going today?

Alex: Thank you, Kirill. Yeah, great to be here. Looking forward to

our discussion.

Kirill: Me too, very much so. And where are you right now?

Alex: Based in Canberra at the moment, so dialling in from home,

doing some errands and speaking to you before heading off

to work.

Kirill: Awesome, awesome. And how is the weather down there in

Canberra?

Alex: It's lovely. Nice, hot day today, quite warm. Nice change from

the recent rain we've had. That should be good.

Kirill: And a lot of people don't know this, but Canberra is actually

the capital of Australia. When I was a kid, I used to think it

was Sydney. Do you correct people often about that?

Alex: I haven't for a while, given most people I deal with these

days are Canberra-based, and hopefully they've figured that

out by now. I have on occasion, when I've travelled around.

Yes, that is a good point.

Kirill: For those listening, there's a bit of geography there.

Canberra is the capital of Australia. How big is Canberra?

Alex: I'm not sure size-wise, the population's about 400,000. It's

quite small in terms of relative, physical size to the rest of

the country. And as you know, it's located almost halfway

between Sydney and Melbourne.

Kirill: And there's a lot of government facilities there?

Alex: Yes, it's very much the heart of the country when it comes to

politics and government departments and agencies.

Kirill: And just looking through your background, I think that

information will be very relevant to our discussion. But let's

start with the beginning. You've got a very interesting and

diverse background, a PhD in mathematics if I'm not

mistaken, applied mathematics, and then you've done lots of

different consulting work and in fact, for those who are

listening, Alex was recommended for the podcast by one of

our previous guests, by Ot Ratsaphong, who heard one of

your talks, Alex, at I think the R User Group in Canberra, or

the Data Science User Group in Canberra, and he found it

really fascinating. So tell us a bit more about that. You run

these user groups for data scientists in Canberra, is that one

of your passions?

Alex: Yes, promoting analytics and data science overall is

definitely a passion of mine. So when an opportunity came

up a few years ago to host the Canberra R User Group and

Data Science Canberra locally, I thought it would be a

fantastic way to not only meet up with the large number of

data scientists and analysts we have throughout the

government space here in Canberra, but also to I guess

spend a bit more time mentoring aspiring and junior data

scientists who often come to me for advice, technical, career

advice, whatever the case may be, I thought it would be a

fantastic forum to actually get everyone together and just

share ideas and speak about what we're doing, which often

wouldn't be so easy to share those ideas and to see one

another outside of conferences that may occur.

So Canberra is quite unique in the sense that we have a lot

of really great people working in different departments and

agencies, but sometimes they're working on their own, or in

small teams, so they have very little oversight on what

others are doing, and sometimes I'm quite surprised to hear

that someone else is working on a similar problem, or is

working on something that's exciting and that they'd like to

get into and want to ask about.

So I tried to invite speakers who are doing something quite

interesting, I think it will apply and appeal to most people,

invite them along to have a chat, and share their ideas, and

it normally works quite well. People seem very happy to

attend and to reach out to one another to share stories, war

stories.

Kirill: That’s awesome. And how often do you have these groups?

How many times a month?

Alex: Yeah, it varies. I try and do one every month or two,

depending on my own availability. In my previous role I was

travelling quite a lot, so that made it difficult, but now that

I’ll be spending a lot of more time in Canberra I’ll try and do

it every month or two, at least have one of them running

every month, if not more frequently. That would be ideal

actually.

Kirill: And so when these groups get together, you have a speaker

or a couple of speakers who present or do you do some

exercises? How do these groups run?

Alex: Normally it would involve one main speaker and then myself

or someone else doing I guess a small introduction

beforehand to just give a small oversight of what they’re

currently working on and what may be of interest, and that

would lead into the main speaker and then have a lot of

questions. Question/answer session after that. And then

people would tend to mingle before and after just to catch up

with people they know or just to ask them informal

questions on what they’re working on. It’s quite relaxed and

casual in that sense. That tends to work well. A lot of us

tend to be introverts and we prefer these more informal

sessions to talk to one another and to get some advice or

just to share our own views. And it’s often a lot of fun, yes.

Kirill: Oh, fantastic. I’m going to play the devil’s advocate here. In

this day and age where everything is interconnected online

and there is plenty of resources and plenty of forums where

people can go online to find mentors or to connect with

others, find out about their work, talk and so on, how is

catching up in person better, how is it more beneficial, and

why do people get more out of it than just online interactions

that are readily available to them at any time of the day?

Alex: That’s a good question. I think in reality people use both,

both methods of communications, to learn. They’re both

fantastic and have a lot to offer. 24/7 access via the digital

platform is incredible, you can’t knock that at all, but I

guess being human and social creatures we love to actually

be able to speak face to face with people, get that immediate

response. And speaking to someone, asking them questions,

you can read their body language, which sometimes is very

helpful when someone is trying to answer a question about,

“Should I take this particular job that I’ve been offered?” or

“I’m having a problem with some technical issue. Do I have a

chance to solve it?” I think people are a bit receptive to the

human elements than they may be on a forum where you

can get a lot of negative feedback at times which isn’t always

helpful, a lot of criticism depending on the forums, so I think

there’s space for both and as humans, we appreciate both

streams. I think it’s a good thing having both open to us.

Kirill: Okay. I totally agree with that. I think you’re right. And that

human element, I don’t know, it has some magic to it that

you just can’t get online sometimes.

Alex: That’s right. When you see someone speak about a topic that

you’re interested in and passionate about, I think being

there can excite you and inspire you a lot more than just

reading about it online or hearing a recording sometimes.

Feeding off the people around you and the vibe in the room

can be quite powerful.

Kirill: Totally. So what would you then recommend to people who

are not yet attending meetups? I’m certain there are people

listening to this podcast who haven’t ever attended a data

science meetup. They like their profession, they go to work,

they do their job, they meet people at work, but they’ve never

gone out of their way to actually connect and meet others

through a meetup like this. What would your advice be for

them?

Alex: I highly recommend that they give it a go, maybe start one

themselves if there isn’t anything like that in their local area

or community. I think it’s quite easy to set up a meetup site

online, get a mailing list together, use your contacts and

networks. Otherwise definitely attend. There’s a lot of

specialty ones I’ve noticed within data science overall –

there’s deep learning ones, ones on machine learning, R,

Python, whatever the case may be. Pick one that you’d be

interested in. You may have a lot to offer that you don’t

realize. You may be able to learn a lot from your peers.

They’re normally quite short and very informal sessions, so

go along and you might be surprised by how much you enjoy

them.

Kirill: Fantastic. Any recommendations on where to find these

meetups if one is not arranging their own?

Alex: I think just look up the website, the Meetup website, and

have a look in your local area, do a search, or reach out to

your contacts and ask if they know of any as well.

Kirill: Meetup.com, yeah?

Alex: Yes.

Kirill: I was actually surprised at how very interesting and broad

that website is. I was in San Diego a few months ago and I

had nothing to do in the morning and I wanted to go do

some yoga. I looked up ‘yoga meetup’ and literally the next

morning I went to a yoga meetup and it was amazing.

Alex: It’s incredible.

Kirill: Yeah, it’s a really cool place. Okay, we jumped straight into

the meetups discussion. But now let’s rewind a little bit and

talk about your background. Walk us a little bit through it.

You started with a Bachelor’s degree in Math and Computer

Science. Let’s go from there.

Alex: I did a double degree, Mathematics and Computer Science,

which was quite new at the time, there weren’t many

universities in Australia offering an actual double degree

versus a double major.

Kirill: What’s the difference?

Alex: Double degree is you walk out with two degrees. I guess they

synthesize the six years of the two separate 3 year Bachelor

degrees into one 4 year degree. That has its own challenges

obviously, taking extra credits, but the reason I did that was

I really enjoyed mathematics a lot, worked hard and did well

at school, so I wanted to pursue that to learn more. I had no

specific career aspirations in mind when I did that. And the

computer science element, I was getting into the

programming and I thought it would make a great mix. I

thought math on its own wasn’t enough, I wanted to do

something else so it was either maths and physics, or maths

and computer science, and I thought, “Yeah, I’d love to learn

a bit more about coding and that might come in handy one

day,” which it very much has.

But throughout that, I have to be honest and say maths was

more my passion. After that I did an honours degree in pure

mathematics, which was very interesting, especially some of

the advanced algebra theory I did, some of the more

complicated stuff I’d studied in my life. And then after that I

was considering a doctorate and some of the applied maths

that I studied, I really enjoyed the element of applying maths

to the really world using both the mathematics and

computer science elements of my degree, and I ended up

doing a doctorate in applied mathematics, which is actually

with the CSIRO - Commonwealth Scientific and Industrial

Research Organisation, Australia’s premier science

organization, and that entailed looking at heat transfer in

grain silos, which was a fascinating topic.

Kirill: Sorry, what was that in grain silos?

Alex: Heat transfer throughout grain silos.

Kirill: Wow. That’s very applied for sure.

Alex: Very much.

Kirill: Okay. Any interesting discoveries there?

Alex: The aim of the research was to look at regions within the

grain silo where particular insect infestations were

occurring. And given the insects are quite small, the thermal

devices they had at the time to measure the heat in those

areas, it was too large to pick up the heat distribution, so it

wasn’t sensitive enough. So the only way we could actually

try to determine what the heat was and [indecipherable

16:00] was to actually do some mathematical modelling, so

hence the heat transfer component, and the idea was that

we discovered the insects were localized within certain

regions, which meant that you could, at a lower cost, only

heat those regions to kill the insects using either microwave

heating or just high heat methods, and that way you

wouldn’t have to invest in heating up the whole grain bulk to

disinfest because the chemical methods they were using

were being phased out globally.

So we determined that you could use microwave heating or

just use large heating elements to kill the insects on the

outside periphery without damaging any other properties

within the grain bulk. Destroy the insects, keep your costs

down, and then go forth and export your grain throughout

the world, which was the main driver in this case.

Kirill: Wow, that’s really cool. So was that research applied in the

end?

Alex: Yes, it was. The government was using that to determine

which regions of grain silos and grain bulk structures they

could disinfest at a lower cost, which was great for them and

for the farmers that were actually looking at disinfesting

there in the grain holdings.

Kirill: Wow! Congratulations! There you go. People eating bread in

Australia, you might have been influenced by Alex’s

research.

Alex: Also around the world, because what was happening is we

would be exporting to a country and we would disinfest with

a chemical method which would only have say, a 99% or

99.5% rate of killing those insects, so by the time the grain

has been shipped to another country, that bulk could be re-

infested. So we needed better methods to kill a higher

volume of those insects. And what the biologists were

finding, because they were actually, depending on the

ambient temperature, theya would move into different

portions of the grain bulk, which the chemicals weren’t

always at a high enough dosage, so being able to just target

those areas using heat was quite an efficient way to actually

exterminate all the insects.

Kirill: That’s so cool. What I really like about this example is—so

this was back in early 2000s?

Alex: Yeah. Quite a while ago, yes.

Kirill: So what I like about it is, right now I think most people

would agree that we would call that data science, very data

science kind of problem, solution and so on, but back then,

it was applied mathematics. Don’t you find it interesting how

the field of data science didn’t exist back then, but you were

already doing data science?

Alex: That’s true. The label in some ways has changed, and also,

as I’m sure you’re very well aware, the computing power we

have at our disposal these days which has really shaped the

world of data science and given us a lot more freedom and

power as to how we tackle these problems. That was

probably more mathematical in the sense that the equations

I was solving, the methods were semi-analytical and

numerical, whereas a lot of the work we’re doing these days

in data science is very much numerical.

That’s the shift I’ve noticed as I’ve progressed throughout my

career and tackled different problems, I’m doing less of the

analytical and semi-analytical solutions to problems and

much more now on the numerical side given the power we

have, the beauty and incredible availability of libraries and

functions through machine learning and deep learning. So

that has been the big shift I’ve seen, and quite an interesting

one for me too.

Kirill: That’s very interesting comments. I don’t often stop to think

about that, that back in the day you had to come up with

ingenious approaches to minimize your computational cost,

whereas now you don’t really care, you just go for it.

Alex: Exactly. Distributed systems, parallel processing, it’s

fascinating.

Kirill: And with the advancement of quantum computing, do you

have any comments on that, on how we’re going to move

even further into that space where we’re just going to throw

machine learning at anything and just brute force the

results out of it?

Alex: I think we really don’t know what we’re going to discover

with that revolution. It’s going to be amazing to see.

Hopefully it occurs in my lifetime. I think it will open up a lot

of doors in terms of the problems we can tackle and how we

can solve them, and more importantly I think it will allow

the broader public or the broader industry to really see how

they can apply the power of data science and analytics to

their own problems to find innovative solutions. In the

health space, I think there’s a lot more being done in that

world, of course physics – the traditional areas where

analytics was heavy. Computational power is being used in

astronomy, theoretical and practical physics. Yeah, I think

that will be quite interesting to see what happens there.

Kirill: Yeah. And do you think that we will have quantum

computing laptops in the next decade or two decades?

Alex: Hopefully. You need to speak to a quantum computing

expert on that. I would love to know. I’m hoping that does

eventuate. That actually reminds me of one project I once

worked on during my undergrad. It was in optical sciences,

so we were looking at creating circuit boards using light

effectively to transmit information on the circuit board

rather than etching copper circuits to make them much

faster. That was I think some of the early work being done

heading towards quantum computing. So you would alter

the refractive indices on these parts on a circuit board

effectively, you’d use light to transmit the information, so I

think that was a precursor to a lot of stuff that will happen

in the future. That was fascinating.

Kirill: Yeah. And I think I’ve heard of similar approaches. They

were maybe 10-15 years ago and now we’re heading into

quantum computing space.

Alex: Yeah, almost 20 years ago. It brings back memories.

Kirill: That’s really cool. Okay, so then what happened after your

PhD?

Alex: Sure. So, I spent a brief stint as an academic deciding will I

use my powers for good or evil. Do I stay in academia or do I

go into the real world? I had a couple of professors pull me

aside and say, “The world the academia is changing. You

may want to think about heading off and doing something

different.”

Kirill: On to the dark side. (Laughs)

Alex: On to the dark side before coming back in the future. So I

thought I did the right thing morally and went out to make

some money. I guess there were two main reasons for that.

One was I’ve been in academia for almost a decade doing

undergrad and postgrad studies and some teaching. I was

enjoying that, but I felt like I needed a change. And two, a

guy who I’d done a PhD with, he was a couple of years

ahead, he went over to the real dark side, he went over into

investment banking, and he talked to me about these

fascinating problems that they were solving and how you

could use mathematics and computer science to actually do

something meaningful and I thought, “Okay, that sounds

really interesting.” I’d done a course in derivatives pricing

and I thought that was quite cool, you know, I get to use my

maths and computer science skills to do something

interesting.

So off I went into the world of financial services. Initially I

spent about a year as a lead quant in a fund of hedge funds

when hedge funds were quite sexy and the rage, which was a

great way using my skills to look at portfolio optimization

and trying to understand how to actually make more money

for the organization I work with, help them make more

money by looking at the distribution of your own

investments – in this case it was investing in hedge funds, so

that posed some really interesting challenges.

And then after a year I was approached by an investment

bank to go and actually do some front office quant work of

derivatives pricing, share pricing, and I spent almost 6 years

doing that, which was incredible. I learned a lot. That was

probably the highlight of my career in many ways in terms

of—from a technical viewpoint it was very challenging, but

also very exciting.

Kirill: I’d like just to pause you for a second, because I’m looking at

your LinkedIn and you mentioned that you used some

modelling techniques including Monte Carlo simulations.

Alex: Yes.

Kirill: I’m not an expert on Monte Carlo simulations, but I’ve done

some work with them, and I find that approach so

interesting. Would you care to share some insights about

Monte Carlo with us?

Alex: I guess in a simple way it’s very much like rolling the dice. I

used to explain to people it’s looking at a brute force

approach to try and solve an equation. So you might do a

million or 10 million simulations on all possible results that

you can have and you effectively average them out in the

end. So at the time, before we had the power of machine

learning that we do today, we had to try and solve some

quite complex problems numerically, some of them we could

solve analytically and semi-analytically, which was great,

but the others we had to take a numerical approach and

often in that space, in the derivatives pricing and the

financial world, Monte Carlo was quite a popular way and an

effective way to actually come up with those solutions.

I found it quite interesting to use because you were looking

at some of the fundamental mathematics through a

computational solution. And I think in some ways, if I can be

so vague, it was kind of a precursor to a lot of the machine

learning we’re doing today, especially the more brute force

approaches. It’s something I haven’t touched since then, to

be honest. I predominantly used it in that career and haven’t

had to think about it for many years. It’s interesting that you

bring it up. It’s good that you bring it up. Actually, I wonder

how much it’s used these days given the power we have of

machine learning.

Kirill: Yeah, it’s interesting. I’ve talked to a few people who’ve used

Monte Carlo, but not as much. Still some use it in finance,

but I’ve discovered that some biologists use it in modelling

evolutionary—

Alex: [indecipherable 26:10]

Kirill: Yeah. So one TED Talk I was listening to, what they did I

think is they were modelling—okay, so do we see the world

as it is, the world around us? Is the table I’m sitting at, is it

actually white, in reality does it exist the same way I see it?

So what they were modelling, the theory they were trying to

prove, was that this table or whatever we see is actually a

mind projection and in reality these things might be

completely different. Like, a tomato might not be a tomato, it

might be something else, but our brain makes it look at it as

it’s red, it’s this form, it’s this smell, whatever, because it’s

good for us to eat it.

So they were modelling, like, “Let’s see if there’s a species

that sees the world as it is versus a species that sees the

world as the brain tells you to see it, and which one will

outperform the other one.” And they used Monte Carlo to

run those simulations to see on average who is going to win.

Alex: That’s fascinating. It reminds me of Schrödinger's cat. That’s

incredible.

Kirill: Yeah, very interesting examples of that. Any idea why it’s

called Monte Carlo? I don’t think I’ve ever answered that

question.

Alex: I once heard it came from being done in Monte Carlo itself.

I’m not sure if that’s true or not. It could have been based on

some of the techniques people were using. Yeah, it’s heavily

based around repeated random sampling, the process itself,

so maybe it does come from the gambling world, I’m not

sure.

Kirill: Okay. Well, there we go. If anybody is interested, Monte

Carlo is a pretty interesting averaging method. But let’s

move on. You spent almost 6 years in the commodities

space.

Alex: Fixed income, currencies and commodities, yeah. What was

interesting about that is I was there during the GFC, so it

was pre- and post-GFC.

Kirill: Did you notice any change?

Alex: I did. Pre-GFC, the appetite within the investment sector

from our clients leading up to it was looking at more

complex, more intricate, exotic options in derivatives that we

were pricing, so that was very challenging for us in the

quant space, for my team, actually looking at more complex

and complex problems to solve, so we were often having to

reach out to the academic world and read journal articles to

try and look at inventions being made in that space and how

to turn those often theoretical solutions into practical

problems.

That was really challenging and interesting, but the problem

was we couldn’t publish any of these in the IP. That was a

shame because we came up with some really interesting

solutions to a lot of these problems that we were facing that

others would have benefited from as well, but of course we

didn’t want the competition to get ahead of us.

Kirill: That’s the price you pay for going to the dark side.

Alex: Very much so. And then post-GFC, that appetite waned.

What we were pricing were more of the vanilla-based

products, the simpler options, so that became I guess for me

less challenging. Having spent quite a bit of time there, I felt

like I needed a change. I was doing more mentorship, so the

management side was interesting me a little bit more, but

not just doing the daily grind of just hacking away at

problems and coming up with intricate solutions. I wanted

to share my knowledge and experience a bit more.

Yeah, that made me decide to take a short break and then

move on. I felt in some ways burnt out after that. Even

though I greatly enjoyed it, I worked with some fascinating

people, some of the best quants in the country, I just really

wanted a change so it was great to have a chance to have a

short break and then move into another role.

Kirill: That’s very admirable, you know, for anyone to have the

courage to say no to a senior quant position, because that’s

such a sought-after position, and a lot of people even in the

space of data science think that that’s a dream job, to be a

quant or even a senior quant at a bank. The perception is,

“Once I get that job, I am going to be set for life, I will be

happy and so on.” But as you say, sometimes you just want

a change. You only have one life, right? You want to grow all

the time.

Alex: Yes. And I grappled with the moral issues as well. I wanted

to use my powers for good. I wanted to be able to—a long

time ago I wanted to share some of that knowledge and

experience to move into a government space, which

happened years later. But that was something that I was

thinking about at the time, and it was a difficult decision to

make. It’s a very difficult role to get into. Once you worked

up and built that reputation and experience, it’s very hard to

turn your back to that. And I still get asked to this day,

“Why the hell did you leave? What were you thinking?” But

as you said, life is about more than just one job or one

career. I think it’s important for me in particular to move

around, learn new things, meet new people.

Kirill: Exactly. And not to say there aren’t companies that provide

that. You know, in one company you might grow and learn

and do different things, but if you feel you’re stagnating,

then why not?

Alex: Exactly.

Kirill: Awesome. We’re going to have 500 people quit their jobs

after this. (Laughs)

Alex: I’ll be blamed for the next GFC!

Kirill: (Laughs) Yeah. By the way, with the GFC, I wanted to ask

you, why do you think they made the shift from complex

products to simpler products?

Alex: Risk appetite wanes. People weren’t willing to take as much

risk given the heat on the banks at the time and what was

happening in the banking sector, especially in the U.S. with

the large banks folding and struggling. Investors wanted

something a bit safer, a bit more secure. As you know, in

that space, high risk is high return, so people wanted a bit

more stability and safety so there was less focus on quick

wins and a real large appetite for risk. And also, some of

those products were based on short-selling, which a lot of

issues occurred, so with less interest on that, there was in

some ways less complexity with some of the models we were

actually trying to price. At least in Australia, that’s what I

saw happen for quite a while.

Kirill: It totally makes sense. I can see how people wouldn’t want to

buy those—what are they called, credit default swaps?

Alex: Yeah.

Kirill: Yeah, the main cause of the whole crisis in the first place.

Alex: Yes, unfortunately.

Kirill: Okay, so what happened after that? How did you get out of

the dark side? Where did you go?

Alex: I didn’t have a specific goal in mind. I just got to a point

where I thought I’d like to take a short break, travel a bit,

and just unwind from that. I hadn’t really taken any leave

during that period. So I took a short break and had many

offers, a lot of similar roles immediately come up, which I

thought, “I better not be swayed into that, I really want to try

something different.”

And I was told about an interesting role to move over into

the insurance space, which is a sector I’ve been thinking

about because I’ve worked with a couple of actuaries, or

people who are former actuaries. I didn’t want to necessarily

do something as technical initially. I wanted something that

was a bit more varied, so the role that I ended up taking was

a management position where I was managing a marketing

analytics team.

There were two elements to it. There was managing that

team, helping them with their marketing, looking at

customer churn, acquisition, the usual things you look for

in that space, but also what I guess is termed these days as

a lead data scientist role and also helping actuaries with the

more technical problems.

But what’s particularly interesting is that’s when I first

became aware of a lot of these techniques that are these

days known as machine learning techniques. That’s when I

first heard about that. People approached me and said,

“We’re looking into this. We’re trying to understand some of

the statistics behind this. Can you just help us out and we

can bounce some ideas and learn together?” And that’s how

I got into what is formally machine learning and data

science, moving away from the traditional analytics, from all

the mathematical modelling, stats modelling, into the more

computational side. So I did a little bit of that in that role

along with looking at customer insights and marketing

strategies, and then from there I transitioned into the

government space where I did most of my machine learning

and data science.

Kirill: It’s interesting to follow your career because on LinkedIn I

can see the dates and it’s like this transition happened for

you as data science started becoming more popular, around

2010/2011.

Alex: Yeah, that’s an interesting point. I guess I can be held

responsible for that.

Kirill: (Laughs) There we go.

Alex: Yeah, it’s funny that it happened that way. I guess I could

see what was happening and the different opportunities that

were coming up, and it just sounded very interesting to me

so I pursued it more from that point of view, as I’ve done

anything in my career, out of interest and what I’d like to

work on as opposed to having a specific career goal in mind.

Kirill: Interesting. So that brings us to Canberra, to the

government work. As we mentioned at the start, Canberra is

400,000 people and 399,000 of them work in the

government.

Alex: (Laughs) It does feel that way, doesn’t it?

Kirill: Yeah. So, what kind of work were you doing? Again,

whatever you can disclose, because I know there is some

probably sensitive topics there.

Alex: Sure. That’s understandable. So, the first role was effectively

around risk profiling, looking at predictive modelling

techniques primarily to try and find bad people, categorize

them in some way, whatever the department agency

categorized as bad. In that case it was with the Department

of Immigration and Border Protection, so trying to find, pick

out that small number of people that are trying to get into

the country illegally or maybe they’re part of some drug

syndicate or whatever the case may be. So looking at some

advanced techniques to try and pick them out, moving away

from more traditional approaches, looking at traditional

techniques, looking at people using their own tacit

knowledge or anecdotal evidence to try and profile a person.

They are looking at high-powered analytical methods to

actually target them better, to minimize how many good

people you actually catch on the border to interrogate and

interview and then to increase the number of actual bad

people you end up catching. So, yeah, that was fascinating. I

spent over two years there and worked with some really

interesting problems, helping develop systems that are used

to this day at our borders to protect our country, which I’m

very proud of.

Kirill: That’s fantastic, very exciting to hear. I’m sure a lot of

Australians listening to this will be excited to hear that data

science is making our country safer.

Alex: Yeah, that’s right.

Kirill: I hope all other countries are following in the same way.

Alex: I’m sure they are, yeah. We did a lot of liaison work with our

fellows in other Commonwealth governments and there was

a lot of interesting work that was being shared and that was

great to see.

Kirill: Fantastic. And then I noticed you moved into a different role

with the government. What caused you to move?

Alex: Once again, that was looking at new opportunities and in

some ways career progression, because the next move

involved going into a department that was much more

immature in terms of their data science and analytics

capability. So I was brought in to try and help push that

agenda forward and to help set up a platform, a cloud-based

platform looking at Hadoop and R, integrating that to really

increase their power of their analytics capability. They

needed someone to help build that up, to promote that, to

get people inspired as to what can be done with that, come

up with some proof of concepts. That was really interesting.

A lot more management in that role, managing staff,

projects, dealing with senior execs, and really helping spread

the word of data science and the power of data science,

which is something I do a lot of these days. It’s not just the

technical work which I find interesting and challenging, it’s

really promoting what can be done. There’s a long way to go,

I think, especially in the government space. I’m sure in this

country, like many others, it’s getting people to feel

comfortable with what you can do with analytics and not to

be scared of it. They see it as a black box often. It’s building

trust so they can trust the methods, the systems. And that

can often be a challenge, but quite a rewarding one when

you see people have their aha moment, “I can see why you

do it this way or why this works,” so a lot of my time is spent

educating these days, which I really enjoy.

Kirill: That’s fantastic. You mentioned on this podcast that you

would be happy to share about building a successful data

science practice from a management perspective. I think

maybe this is the right time to go a little bit into that

discussion. What tips or advice can you share for people

trying to build a successful data science practice?

Alex: Sure. I think there’s three key elements to that, which I’m

happy to go into the detail. For me it revolves around people,

the value you can add, and communication. On the people

side, it’s most imperative to have the right people, the right

mix of people in your team, to have the data scientists,

quants, whatever the case may be with whatever industry

you’re working in. They’d have to have the right technical

skills, people that have proven themselves throughout their

career to not just be able to do the technical work, but to

communicate it to yourself, to the broader department and

agency. In some roles it’s enough to have the people that are

stronger in a technical way, that can just sit at their desk

and hack away at code. We definitely need those people and

they’re highly valuable, but sometimes you need people that

can engage with the stakeholders to collect the functional

requirements effectively and then translate them to technical

requirements.

It’s having the right people with the skills to do the data

wrangling, the modelling, the engineering side to embed the

models into the enterprise-wide system. You’re having a mix

of a person that can do all that. You can’t build a successful

practice without the right people.

On the people side, you definitely need a willing coalition of

support from within the department, agency, organization,

whatever the case may be. You need support from your

peers and from senior management. Without that, you really

won’t be effective. Because the goal of data science and

analytics is to initially develop insight from data, but more

importantly it’s to make that actionable. Without that you

are pretty much just doing academic research in many ways.

Unless you have support from senior management to turn

those insights into actions, I don’t think the actual data

science practice will be effective or successful in the end. So

it’s really important to have that.

I’ve noticed many cases where people just take on data

scientists just to build teams more for the vanity reasons

rather than actual need or actually wanting to support it,

and that’s where it often fails and where a lot of challenges

occur. So if you’re going into a job, building up a practice,

make sure you have that support from higher up above.

Otherwise your efforts may be wasted in the end.

On the people side, it’s important to be a data science

evangelist to really show the benefits, to educate people

about what it can actually provide to them, how they can

personally benefit. I think that’s very important. People don’t

always see that connection so I think it’s important to take

on that inspirational role, which can be hard for some

people. They don’t feel as comfortable talking about what

they do, but it’s important to share your ideas, to

communicate, to always become a marketer of analytics and

data science, so inspire others and create meetups or

informal groups within your own organization, attend

meetups to see what other people are doing and get advice

from them. I think that’s quite imperative to the success.

Also the hierarchy can be an issue. Areas where it’s very

much hierarchical, I’ve found don’t work as effective in these

technical teams as opposed to having a more flat structure

where there’s more autonomy and flexibility. I think that

tends to work much better and data scientists prefer to work

in those environments. And also, you need to know how to

manage—for management to know how to manage data

scientists, because their career aspirations can be quite

different to the rest or to more generalists, say.

So keeping them interested, engaged, giving them access to

the right people, tools, software, whatever the case may be,

is very important. Normally it comes around making sure

there’s data for them to work on and challenging problems

for them to solve. Without that, you’ll lose those people,

you’ll have such a high churn rate, which I’ve seen many

times where I’ve managed teams and tried to hold on to

people or to bring people on. It really depends on the

organization, the problems they’re working on, and that’s

sometimes what drives me out as well. If I don’t have

interesting problems or enough data, then I’ll move on

myself. So that’s on the people side.

On the value side, the most important thing there is to be

seen as a trusted professional and not just a technical

genius. A lot of people that build these teams or work in

these teams, and rightly so, they want to be seen as the

technical gurus, that they can be approached to solve these

problems. You don’t really get the practice off the ground

unless senior management and your peers actually trust you

as well. So part of that is to share knowledge, to educate

those around you, inform them and be transparent about

what you’re doing and why you’re doing it, don’t just be a

black box, show them how you can help them and that you

want to support them and how you’ll go about doing that. I

think that’s very important.

And also what is very much key is to link the outcomes, the

work you’re doing, to the strategic goals of the organization.

Sometimes you have to make that connection quite clear,

what you’re doing and how will that benefit the organization,

how will customer churn, increase money, increase value to

the public or private sector, whatever the case may be.

Making that connection clear, always having that connection

in the back of your mind so you can use that when you’re

speaking to senior management, I think is paramount to

actually having them trust you and believe in you and throw

money and resources at you to actually try and solve their

problem.

On the more technical side around adding value, I think it’s

important for the analysts to develop software development

practices, which I see less of these days with people coming

into data science without having the more computational IT

background. They’re not used to doing things like unit

testing, peer code reviews. These days, with things like

github, that’s all becoming more popular, but for a while the

source control was something no one had ever thought of.

They’re just developing models on their own, systems stored

where no one else can see the code or debug it or do

anything. So I think those practices are very important to

having the right team and for the team to add value to

organization.

And I think using prototypes to overcome doubt and

resistance is very important because often, as we face a lot

of doubt and resistance from people around us as to how

we’re building this model, improve what we’re doing. We’ve

been doing it for years. A traditional one is, “Why is your

method any better or any different?” So building a POC to

show people, “Look, this is the insight we’re getting from the

data. This is what we can do quickly. This is the actual value

that we can add. How about we invest more time and money

to actually develop a full-blown system?”

Another important thing is to put people before technology.

It’s more important to have the right people rather than first

investing in some software solution that a vendor is pushing

and then getting people to adapt to that, which I’ve seen

happen a lot in my career. Getting the right people, let them

choose what is possible – it’s not always possible depending

on what you’re working on for security reasons, funding,

whatever, but ideally you want to get that first.

[indecipherable 46:37], worry about technology if you’re

building the system from ground up because the good people

will tell you what the need, they want flexibility. These days

it’s really moving towards open source, even in the

government space, which is great to see. We’re using R.

Python, Hadoop platforms rather than the traditional SAS-

based systems, IBM, etc. So that’s very good to see. It gives

you a lot more flexibility and it’s cheaper for the organization

department.

Another key thing is don’t be afraid to fail, or fail fast and

cheap. I think that’s important as well, don’t be shy, try new

ideas, developing the mindset like a hacker’s mindset of just

giving something a go, see if it works, work in a more agile

way, and then move on. Don’t just put all your eggs in one

basket or just have this one solution at the end. Work in a

more iterative fashion and make sure that the people around

you are comfortable doing that, and that senior management

understands and your stakeholders understand that you

want to interact with them a lot more. That interaction, I

think is very important, interacting with the business. You

can’t be isolated. You need to be constantly engaging with

them, understanding their business world.

That is key, to understand the business and then to go back

and develop solutions. And what often helps in those cases

where I’ve led those teams is to have some of my analysts

embedded in some of those teams, either in IT, in the

business unit we’re working with, in some of our stakeholder

teams, just to make sure there is a constant flow of

information back and forth. That’s what often really

increases the chance of success.

And the final one is around communication. So, one key

point there is to focus on the outcomes, not the methods

and tools used. So when you’re communicating as a data

scientist or a leader of the data scientists to other people,

talking about what the real outcomes are, something that

they can understand, use their terminology, understand

their jargon, and don’t worry so much about the tools and

methods you’ve used. That’s important to you, but may not

be the main focus from their point of view.

Try to communicate those ideas very clearly, limit the jargon

you’re using, but use their business lingo so they

understand. You all want to be speaking one language.

Visualization is often important with this. These days,

having tools like Tableau and Qlik and SAS VA is a great

way to show people some of the solutions you’re actually

coming up with. Visualization, I’ve noticed, works very well,

especially when you’re talking to people from a less technical

background.

And building trust within them also helps sometimes when

you’re doing roadshows. I used to do a lot of stakeholder

roadshows within an organization, go around to the different

teams and show them what my team can do for them, what

we can do. Let them pose particular problems and we’d say

to them, “Okay, give us a week or two, depending on our

own timing, to try and come up with a simple solution or a

roadmap that we can work together on doing a proof of

concept for you.” And often that’s great, because people then

are more open in those informal settings to discuss ideas, to

come up with questions, you know, “We’ve thought about

this. Is this possible? Does this fall into your realm?” You

open up that dialogue, that communication. It shows them

that you’re keen to learn about their passion about what you

do, and it gives them a chance to ask questions face to face.

It goes back to what we were saying earlier about the

meetups. We as humans are a lot more comfortable,

especially with these technical issues, talking face to face.

And what you find sometimes is people can be embarrassed

about asking a question. They’re not sure if it’s a stupid

question or does this technical question mean anything, but

once you allay those fears and they see you as just another

human as well, not just a technical genius or a geek, that

dialogue opens up and then often it becomes quite

successful from that point on. So hopefully that ramble

answers those questions and provides some insight as to

how I’ve managed to, at times, build quite successful teams.

Kirill: Wow. Alex, that was amazing. I was listening and at first I

was writing down everything you’re saying, so much value. I

was writing down from the people side and the value add

side and then I just ran out of space on my paper. (Laughs)

But I think it’s so valuable. If you don’t mind – the good

news is this has all been recorded – I’ll ask someone in the

team to put all of this into an infographic and then we’ll

share it on the page. So, guys listening to this, you just go to

the page of the podcast which you will hear at the end of the

session or at the start of the session, and you can download

the infographic absolutely free and we’ll share it on

LinkedIn. I think this is super valuable for people building a

data science team.

Alex: Yes, it is. If we have time, I can run through something

similar on actually becoming an effective data scientist,

which is something that I’ve often been asked by people. I’ve

got a similar list that I tend to work through in my head.

Kirill: Please, let’s do that. We definitely have time and let’s do that

again. I’m sure this is going to be super valuable on the

flipside, for those who want to not just build the data

science team but be the data scientist. So, here we go. How

to be an effective data scientist?

Alex: In my view, anyway. So, there’s four particular areas I like to

break it down to: that’s looking at skills, the business side,

communication and attitude is quite an important one, I

feel. So from the skills side, you need to have strong

quantitative skills, of course, to become a great data

scientist. The main point there is to build up those analytic

capabilities, not so much on the tool side, but how to think

about problems, the problem-solving logic involved. A key

element of that is to ignore the math and stats at your own

peril. I don’t expect people necessarily to go out there and

get a PhD in mathematics and statistics and to understand

all the fundamentals in great detail. An important thing, as

I’m sure you’re going to agree, Kirill, is to understand at an

intuitive level. I think that really makes or breaks a person

as an analyst in general throughout their career.

I’ll give you one quick reason or an anecdote as to why that

happens. I was once mentoring a junior staff member

looking at solving a particular semi-analytical solution to a

problem. And they had the answer and they said, “Look, I’ve

got this answer now. How do I know if it’s right? How do I

actually test this?” And I said, “Well, first of all, you should

be using common sense, and I can tell you your answer is

wrong.”

And he looked at me and he said, “How do you know? I’ve

gone through the mathematics, I’ve done the computations

and everything seems to make sense and I’ve done this

many times before. How do you know the answer is wrong?”

I said, “Well, first of all, if you understood the business

problem, you will see that we’re out by an order of

magnitude. The answer was 32 and I was expecting 320. So

obviously there’s a problem there.” That’s one thing. That’s

the business side. They weren’t really engaging with the

business enough to understand. They just took on the

problem and thought, “This is very much now an academic

problem and I’ll go away and solve it,” which was something

they’re comfortable with and they’ve done many times

before.” And I get that. We all fall into that trap at times.

But the important thing is, if they were also to look at the

structure of the mathematical equation underneath, they

would see why we were out by order of magnitude. So having

that intuitive grasp of what the model is doing helps you

then understand are you getting the right answer, are you in

the right ballpark, which is often very important. And also,

the structure tells you, “How do I actually go and debug it or

how do I find out where a small change in my input is giving

me this large change in the output which I’m not expecting?”

So after we worked through that, we were quite quickly able

to determine what was happening. And it gave them just a

better insight for how to actually go about solving those

problems. That’s one thing, I guess. I’m used to these days

having less of an opportunity to work on those analytical

and semi-analytical solutions, to get lost in the beauty of

mathematics given that now most things I work on are so

empirical and very much computationally-based. I’m used to

that at times, but…

So, the intuitive grasp still holds true. If you’re working on—

let’s say we’re looking at machine learning, artificial neural

networks. You’re looking at forward propagation, backward

propagation, there’s gradient descent methods, cross-

entropy, cross-functions, whatever the case may be. People

are looking at all these complex-looking equations and then

they’re thinking, “I don’t understand what’s going on here.”

Someone asked me recently a question about—they were

looking at a derivation. They were trying to gauge from me

what does this mean mathematically. One point was to

explain it from an intuitive level, to try and explain what’s

happening. But an element of that that was really important

was going back to fundamental principles they would have

studies such as calculus, in this case the chain rule, you

know, understanding how forward propagation and gradient

descent work is all around the chain rule.

So once you understand the concept of the chain rule and

what it’s doing, looking at the underlying derivatives, then

you can quickly understand, “Okay, I can see that the rate

at which the weight learns is controlled by an area in the

outputs, so large areas mean I’m getting faster learning in

the neuron itself.” So, that really helped the person grasp

the concept, even if they didn’t look at all the derivation and

do it themselves – I did that for them – but the ability to look

at it and say, “Okay, now I know what’s happening

intuitively with this calculation,” means I have a better grasp

not only of how does the method work, but how can I test

them, my own solutions, is it the right method to use for a

particular problem. That’s all paramount and really

important to actually becoming a strong data scientist.

That goes back to how do things work intuitively, which I

think people forget sometimes, but it’s incredibly important

to focus on that as you learn. It helps you learn and

understand how any problem works in life, not just the

mathematical ones.

On the quant side, often I’m asked, “Should I be doing a

Master’s or a PhD in data science?” And my question to

them is, “Why? Do you just like the idea of having those

extra letters after your name, or do you think it’s important

for your actual career?” What I think is more important to do

further studies, if any, in the fundamental sciences, in the

maths, the stats, econometrics, physics, whatever, I think

the maths and stats skills (or actuarial studies as well) that

you gain from that gives you a better grasp of what’s

happening intuitively in the modelling sense than just doing

say a Master’s in data science, which is becoming all the

rage these days that I’ve seen. That might have merit as well,

but I think understanding the fundamentals at a deeper

level will take you further in your career, especially if you’re

moving into a more technical area of machine learning, AI,

whatever. I think that’s much more important.

If you just want to work more on the periphery and

understand what’s happening, then Master’s has a lot of

benefit, but to go deeper, you need to go deeper with the

fundamentals. There’s a lot of great courses online,

SuperDataScience and others, and I try and tell people

about some of that stuff. And going back to my earlier point,

when you’re building an effective data science practice, I

think trying to do some of that education internally is

important. Like, if you were to run some introductory R

courses or data science courses internally, you’ll find that

there’s lots of analysts that are interested and that people

want to build up their skills, so not only do you share your

passion, but now you have extra people you can use within

your organization and department to help spur your cause,

and also to help you with your modelling. You know, you

have extra staff now that you can use to help you with your

workload. I think that’s great. I’ve seen that happen many

times and there’s lot of people that are very keen to learn.

They may not all go and become data scientists, but having

a greater understanding of R or Python or some simple

predictive modelling really helps them set them up for a new

element of their career they never would have done before.

So through you, they’re now going to learn more and share

that passion, which is fantastic.

Another great way to learn and to build your skills is to do a

lot of hands-on on-the-job training, which I think is a

fantastic way to learn. Kaggle, of course, and things like that

are great. At times, new areas that I’ve wanted to learn has

happened in two ways: either putting myself in a situation

where I’m trying to solve a problem in a technical field that I

haven’t really done before, it’s a great way to learn. And the

other one is to actually be asked to teach a course that I’ve

never done before. So what better way to learn than through

teaching?

So, there’s a lot of ways for people to learn these days, as we

discussed earlier: go to meetups, ask informal questions, do

a lot of online training, do formal courses… So many options

these days, but on-the-job training, I think, can be quite

important, especially if you’re working with strong analysts

around you, people that are willing to share. And when

you’re forced to work on a problem, that forces you to

quickly learn a technique and try things out, not to be

scared or just to sit back and think theoretically about a

problem but actually get your hands dirty.

The coding side, on the skills side, is very important. You

can’t get anywhere without SQL or the Hadoop equivalent

these days. You need to be able to extract the data before

you do any analysis on it, of course. R and Python are the

go-to languages. I’ve seen a lot more growth in the

government space in Australia, which has been great, not

just moving towards open source, but all the libraries and

functionality available through R and Python has been great

and opening up opportunities on the types of problems that

people can solve – predictive modelling, natural language

processing, AI, ANNs. It’s just fantastic. Even if it doesn’t go

anywhere, people are learning it and trying new things and

they’re moving away from more traditional stuff like SAS and

C#, C++, etc., so more towards the functional programming

languages, which is good.

And I guess for people it’s important on the skill side to just

be familiar with most techniques. Even if you don’t use

them, just be aware of the different things that exist, you

know, natural language processing space, convolutional

neural networks and their implications, text mining, new

advances in predictive modelling, other analytical

techniques. Just be aware at least what exists and

something you may turn to to help you solve a problem at

some point in the future. Just be aware of it. I think it’s good

going to meetups, reading stuff online. It’s a great way to

broaden your knowledge base. That’s on the skill side.

On the business side, the fundamental thing you have to

keep in the back of your mind and to constantly strive for is

to understand the business problem. Understand the

business you’re working with, assuming you’re working in

an area that I’ve—many times when you work with a

business unit, you need to understand their world. You need

to feel their pain, know what their pain points are, where

can you really add value, and then start thinking, “Okay,

how can I use my skill and experience as an analyst to

actually help these guys?”

It can be something as simple as helping them transition

away from doing some sort of high-level analytics and

reporting in Excel to a more advanced system, or do they

actually have a problem that entails itself to a predictive

modelling solution. You won’t know that until you really

focus on their area, their business area, to understand what

problems they face. So that should be at the forefront of

your mind rather than “What techniques should I be using?”

or “What do I want to work on today?” Understand the

business. That adds value to you and it adds value to the

organization.

You have to work with stakeholders, open up

communication and engage with them. You don’t want to be

isolated. As part of that, where often people get caught out is

they don’t have clear objectives defined. You know, someone

will talk about a problem at a very high-level and you think,

“Okay, I have a potential solution to this.” You go away and

develop something after a few weeks, you come back and

they’ll say, “Well, that’s not really what we meant.” “Well,

that’s what you told me it was. Hold on, where are the actual

objectives? Nothing is being written down.” So it’s important

to try from an early point to clearly stipulate what the

problem is and what the approach is to actually getting a

solution. And what will the solution look like? Will it be a

one-off report? Will it be predictive modelling that goes into

an enterprise-wide system that’s run continuously for risk

scoring? Whatever the case may be, try to have that set up

early. Sometimes it’s an iterative process. They don’t what

they want and you don’t know what you’re going to come up

with until you see the data. So work towards that.

And part of that, what’s really fundamental, is make sure

they have data. I’ve been in these situations where people

call me in and they want me to solve their problems using

the fancy world of data science, but they don’t really have

much data available, or they don’t have a large historical

database. I’m quite limited in what I can do in those cases

when there isn’t much data. Or you have data, you have

access to limited data, but you have to wait a month to go

through the security clearance, get all the data sorted. Or

one group gives you the data, but another group, because

their data is always sitting in a disparate system, it’s always

dirty, it’s always messy to work with – how are you going to

join these different datasets?

So understanding the business, understanding where the

data which is important to an organisation lies, who are the

guardians of that data, how to win their confidence to share

it with you? Because sometimes people aren’t happy to

share the data with you, I’ve noticed. They want to hold onto

it, they think the data is their property, as opposed to

belonging to the organization. So often there’s a lot of these

internal battles that you’ll have to convince people that they

should relinquish the data, that you want to help them, and

that you’re there for the greater good of the organization.

So often, when you’re working with a business to solve their

problems, I think it’s important to validate the models and

the analytics you’re doing with the people, not just by

statistics and evaluation techniques. It goes back to this

iterative process of engaging with your stakeholders. Take

them on a journey, tell them a story of what you’re working

on, why you’re doing something, show them interim results,

does this make sense, educate them why it may not make

sense, or what we’re expecting to get at this point. So that

communication and education with your stakeholders is

really important, it should never stop as you work on these

projects. I think it’s very valuable.

That takes us on to the next point, which is on

communication. So when you’re trying to communicate it

with internal stakeholders, senior management, whatever

the case may be, I think it’s important to try and excite these

people about what you’re doing, share the passion, tell a

story, show them how you can help remove the pain that

they’re facing, increase efficiencies, whatever the case may

be. Link it back to the strategic goals as I mentioned earlier

about the organization and try to make it as clear as

possible that what you’re doing will help them. As opposed

to what you’re doing is cool or exciting or is the right thing to

do, but how does it actually help them? I think that’s really

important.

Often I found what works best is to use demos, not so much

leverage the power of PowerPoint slides, which is great but it

can be a bit boring and stale to people. So, try to show them

a demo, get them involved, get them to interact with it,

“Here, change this value,” you know, “Put in the value that

you think is realistic. Let’s see what happens with the

outcome. Let’s look at some ‘What if’ analysis or try and

predict something three months down the line if we change

this particular data point now.”

And as part of that, when you’re talking to them, as I

mentioned earlier, try and adopt as much business jargon

and terminology as possible rather than just revert back to

jargon we tend to use in the data science space. If people are

keen and want to learn about that, that’s great. But if not,

it’s probably bad to try and force it on them.

Because what often happens is people get a bit more

sceptical when they hear some of these big terms that we

use, and they feel intimidated in some ways, they feel

uncomfortable. So try and steer away from a lot of that

technical jargon, use it when you have to, but speak more in

business terms and in a way that really helps them

understand how it’s going to benefit them. So that’s why I

think visualization is often important in that space.

Visualizing something as a human is sometimes easier to

grasp rather than the words that we use, which aren’t

common to everyone.

And that takes me to my final point, which is around

attitude. And I think an important aspect is don’t be scared

to fail, as I mentioned earlier, as a data scientist. Try new

ideas, talk to people. If a particular method doesn’t work, it’s

okay – try something else and no one is going to think any

less of you. It’s a dynamic world, things are changing,

there’s new techniques to try, data can be very difficult and

complex to work with, understanding the business rules

around it is very hard. Once again, having good

communication with the business owners really helps in

understanding those underlying business rules, which is

something that can catch you out, especially in the

government space, where there’s a lot of complexity in these

old legacy systems, data sitting all over the place. How does

it all hang together? What’s used and why, depending on

particular legal policy implications that sit around it? You

really need to understand that when you’re working with the

data and coming up with a model, hence the business is

your go-to for that.

Also along those similar lines with attitude is to adopt, as I

call it, a hacker’s mindset, just try new ideas and don’t be

scared if you’ve never done something in Python but you’ve

done it in R, give it a go in Python, build up your own skills.

You may find another library that you think is helpful, that

may be better or faster, whatever the case may be. Don’t be

scared to get your hands dirty and play around. And on

that, don’t be scared to ask for help when you need it, either

from a peer, from your manager, whatever. Don’t let it go too

long and you think, “Oh, I’ll finally get the answer, it will be

okay,” when this deadline is looming. If you need help with

something, either a business or a technical problem, just

yell out. I’ve noticed a lot of people, and myself at times, are

afraid to ask, think people will think we’re stupid or we don’t

know enough. You quickly realize people do want to help you

to be there for the greater good and everyone goes through

these situations where you just don’t know the answer to

something, so just ask.

And on that too, as I’ve also alluded to, always focus on the

outcomes, not the methods that you’re using. So, it’s

important to build a simple model first, not just go to

something that’s complex and exciting, which is harder to

understand, harder to debug, harder to explain to people. So

just focus on those outcomes and then worry about the

methods used to deliver on those outcomes. And part of that

is really understanding the business problem to help you

strive towards what the correct outcome is. On attitude, you

need to be curious and a problem solver, you need to enjoy

problems, and be an evangelist to really inspire others and

to share the passion for analytics and data science. So that

can be through your own work in formal gatherings, create

your own meetup, write a blog, whatever the case may be. I

think the attitude is an important one, yeah. So, yeah,

hopefully that sums up some of my ideas that I’ve come up

with over the past few years. I hope that helps someone with

their own career.

Kirill: Fantastic. I’ve been listening to this and I’ve learned quite a

few things from here and I really like how you broke it down

into those different parts, about skills, the business side,

communication, attitude. I think they’re all very valuable.

Once again, with this one we’ll also aim to do an infographic

and share, and that way for any listener, whether you’re

trying to create a data science practice or you are trying to

be the most effective data scientist that you can be, you’ll

have something to follow along.

Thank you so much, Alex, for sharing. I’m just curious, how

did you come up with these? You mentioned ‘over the years,’

but did you have a system that helped you develop these

bullet points?

Alex: I guess when I gave a meetup a little while ago, I had to force

myself to think about how to synthesize what I’ve learned in

my career in a way that would help those aspiring to become

better data scientists or to transition to the area, because

I’ve been asked many times, you know, “How do I actually go

about becoming a data scientist?” or “I’ve been doing a

different type of analytics for a while. I want to move into it.

Why should I, what skills do I need?”

And in terms of building effective teams, I guess as I was

building more and more teams, I’d often think about “What

do I need to do in the next role I move into to actually make

sure it’s as successful as it was before or better than it was

before?” So then I started thinking, “Well, I think I better

start jotting down what’s worked and what hasn’t, and then

of course bouncing ideas off peers, reflecting on what’s

worked in the past with managers I’ve had and what hasn’t

worked, what could have been done better.” And just try to

quantify everything, put it down into a nice little framework

that helps me revert back to it or just hand it to someone

and say, “Try to focus on this and then see how you go.

Come back to me if you have any questions. This should

help you get started.”

Kirill: Yeah. That’s very admirable.

Alex: Thank you.

Kirill: It’s great to see how you’re giving back to people who are

starting out and getting there. Hopefully we will help you

spread the word through this podcast.

Alex: I hope so. That would be great.

Kirill: Awesome. Well, we’re out of time, but thank you for coming.

What is the best way people can follow you? If there’s

somebody maybe in Canberra who might want to get in

touch, or somebody who wants to follow your career online,

what are the best ways to do that?

Alex: LinkedIn would be great. It has links to my Meetup groups

as well through there, so they can link to that and then

register as members and then come along to the next

meetups. But LinkedIn is a great way to stay in touch and

ask any questions anyone may have. I’m always happy to

offer advice and help out in any way I can.

Kirill: Okay, fantastic. Thank you so much. And I just have one

last question for you. Is there a book that you can

recommend to our listeners to help them in their careers?

Alex: One book I think would be great for people is written by

someone I know, aa friend of mine who is now Director of

Data Science at Microsoft. His name is Graham Williams

and the book is “Data Mining with Rattle and R.” It’s a great

book in helping people understand how to actually go

through data mining, data science process. It particularly

uses R and a package called Rattle, which Graham invented

himself. It’s a really great GUI for doing predictive modelling

within R rather than having to do all the modelling through

a command line. It’s got a great interface, makes it easy to

learn, and a great way to interrogate and understand the

data, so I highly recommend working through his book and

some examples for people who haven’t done much and

maybe look at some techniques. That’s “Data Mining with

Rattle and R” by Graham Williams. Graham is the guy who

is always happy to help people as well.

Kirill: Yeah, it says a lot that now he’s working as a data scientist,

is that right, at Microsoft?

Alex: Yeah, and for a long time he was the key data scientist at

the Australian Tax Office and he did some fantastic work

there, so he’s likely respected throughout the Australian

data science sector, one of the leads there.

Kirill: Fantastic. Well, there you go. Graham Williams: “Data

Mining with Rattle and R.” Once again, Alex, thank you so

much for coming on the show. This has been invaluable and

I’m sure lots and lots of people have and will get a lot of

value out of the insights you shared today.

Alex: I hope so. It’s been great chatting to you, Kirill. I really

enjoyed it.

Kirill: So there you have it. That was Dr Alex Antic, senior data

scientist at the Australian Federal Government. I really hope

you enjoyed today’s episode. There was lots and lots of

information to share. First of all, make sure you go to

www.superdatascience.com/121 and download those two

infographics. We put in quite a bit of effort into those and, as

you can see, Alex put in all of his life’s and career’s

experience into those, so you definitely don’t want to miss

out on those. And moreover, you might know somebody who

they can help, so feel free to share them around. That’s our

mission, that’s our goal, to help people into the space of data

science, to help spread the word, and we’ll only be excited

and happy if you can contribute to that as well.

The other question I wanted to ask you is, what was your

favourite takeaway from this podcast? Again, there was lots

and lots of information, but personally for me, I was most

curious about the discussion that we had with the shift

that’s happened in the world from analytical to numerical. I

think that was a very philosophical conclusion from what we

can observe in the world right now. Before, you had to be

very smart and cunning about the mathematical equations

you develop in order to solve problems. And now you can

just be less like that and just throw things into machine

learning algorithms and get them to churn the numbers and

it’s still going to work. It’s just like a brute force approach

instead of a very elegant mathematical approach.

That’s exactly what gave rise to the space of data science. If

we didn’t have a machine that can churn that many

numbers and brute force through things like that, it’d still

be called just mathematics, just applied mathematics, and

that’s what we’d be doing. But now we have the power of

data, the power of analysing lots of data and that’s called

data science. Interestingly, it’s continuing, that trend is

continuing. With the rise of quantum computing, we will be

able to brute force even more, we’ll have to think even less

about how to approach problems, just throw everything into

the computer and let it spit out the results and you I guess

will be able to use more and more sophisticated algorithms

like deep learning, which require a lot of computational

power simply because you will have that computational

power.

So there we go, a very interesting way in which the world is

going. And again, your takeaways from this episode might be

different, but in any case, I hope you enjoyed this

conversation today. If you did, make sure to share it around

with others who might benefit from it as well. Don’t forget to

connect with Dr Antic on LinkedIn. You can find his

LinkedIn URL and the link to his Meetup group at

www.superdatascience.com/121. You can also get all the

show notes, including the two infographics there as well. On

that note, thank you so much for being here. I really

appreciate you taking the time to join us for our discussion.

I can’t wait to see you back here next time. Until then,

happy analysing.