February 25, 2020 Careers in [email protected] Grad 2014 Bachelors in Biochemistry...
Transcript of February 25, 2020 Careers in [email protected] Grad 2014 Bachelors in Biochemistry...
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Careers in DataFebruary 25, 2020
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Ravi PatelProgram Director
Health Data Science
linkedin.com/ravikpatel-phd/
Bachelors in BiochemistryGrad 2014
PhD. Molecular Pharmacology2014-2019
Insight Data Science Program director2019- current
Insight Health Data Science Fellowship2019
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Today we will cover● Why are careers in data important?
● What are the different roles and responsibilities?
● Case Study: Uber Eats: Providing personalized food recommendations
● What does it take to get a job in this field?
● Insight Data Science and Transitions
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Why are careers in data important?
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Why are these careers booming?● Information Explosion & Big Data
○ People were ready to share information
○ We had means of sharing the information
● Data Driven Decisions
○ Not taking decisions based on “gut”.
○ Better understanding of users.
○ Personalized services: Recommendations.
● Technology Advancements
○ NGS
○ EHR
○ Drug discovery platforms
○ Digital health
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What can you work on?
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Different Roles in the data space
Data Scientist/ Data Analyst
● Product Analytics● Understanding
Business ● Understanding
Data
Data Engineer
● Data Pipelining● Distributed
Storage● Scalability
Data Product Manager
● Market analysis● Future of Data
products● Business growth
ML Engineer/ AI Professional
● Designing ML Algorithms
● Productionalizing ML models
Data Infrastructure Engineer/ DevOps
● Designing infrastructure
● Automated deployments
● Handling Dev pipelines
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Case Study: Uber Eats
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Copyrights Uber Eats and Wired
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Use case: Provide personalized food choices
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Copyrights Uber Eats and Wired
Update the options that gets shown on the app based on user preferences.
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Use case: Provide personalized food choices● Need to capture user specific data
○ Past orders by the user
○ Famous restaurants in user’s location
○ User search history, clicks??
○ Nearby Orders
● Data Product managers:
○ Identify the need for the product.
○ Work with others to define the development
lifecycle.
○ Define user-testing, AB testing frameworks
for the product.
● Data Scientists:
○ Analyze the User data to find out what
features are important.
○ Run experiments, build out models.
○ Retrain existing models with new data.
● Data Engineer:
○ Define a pipeline to get data to
scientists/analysts.
○ Automate process for new data.
○ Build a scalable data pipeline.
● DevOps engineers:
○ Maintain infrastructure for data pipelines.
○ Define development best practices.
○ Build continuous integration pipelines to auto
deploy code, test them.
○ Define integration tests for the code deployed.
● Machine Learning Engineer / AI professionals:
○ Build a deep learning model to get super
accurate prediction of user behaviour
○ Monitor quality of recommendation
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How do you get these jobs?
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1. Knowledge● Understanding Concepts related to each role
● Knowing the fundamentals
○ Stats
○ Computer Science
○ Math
○ Programming
○ Analytical Thinking
● Knowing the tools
○ Pandas, Numpy, Scikit learn
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2. Skills● What is the industry tackling
● Understand how to apply the knowledge to industry problems
○ Reading blogs
○ Attending meetups
○ Knowing to ask the right questions?
● Applying the relevant tools / techniques to relevant problems
○ Understanding trade-offs
○ Doing Projects
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3. Network● Know the right people to talk to
○ Hiring Managers
○ Technical Recruiters
● Know how to pitch yourself
○ Tailor a pitch for each role you apply to.
○ Use career fairs effectively, don’t just drop in your resume.
● Meet the right people
○ Meetups
○ Hackathons
○ Fellowships/ Internships/ Bootcamps
● Know to have technical conversations (Knowledge about the field will help here)
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Interview Process (Internship & Full-time)● Preparing a good Resumé to get a foot in the door!
● A quick phone screen to get you started
● Take home assignment
○ Timed Coding assignment (HackerRank, Leetcode)
○ Over the phone coding
○ Take home Data Challenges
● Onsite
○ 4-6 hours
○ Mix of Behavioural, Technical and data oriented.
○ Speak to multiple team members
○ Topics: Designing data platforms, Whiteboarding coding questions, Technical conversations
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Most importantly, Be open to rejectionAnd
Learn from your mistakes
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Insight Data ScienceTransition to Careers in Data
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Where are we?
28
Seattle
Portland
San Francisco
Los Angeles
Austin
Chicago
New
York
Boston
Toronto
In Person
Remote
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Insight Offerings - Which one to pickData Science Program
● PhD in quantitative fields.
● Have worked in analysing data.
● Good problem solving skills
Data Engineering Program
● Engineering background.
● Worked on and maintained building engineering systems.
● Java/Python
Health Data Science Program
● Postdoctoral researcher, medical doctors
● Interested in genome sequences,clinical trials.
Artificial Intelligence Program
● Engineering background.
● Have worked on training and deploying ML or NN.
DevOps Engineering Program
● Systems admin and Linux background.
● Problem solver critical thinker.
● Can understand containarized sys.
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New Programs - More focused domainsSecurity Engineering Program
● Designing security measures
● Building secure applications.
Decentralized Consensus Program
● Blockchain technology
● Smart contract management
● Decentralized architectures
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UCSF and Insight AlumMelissa Runfeldt - Senior Data Scientist (Highmark Health)
Dominic Tong- Data Scientist (InsightRX)
Natalie Korn- Data Scientist (AppAnnie)
Peter Chisnell- Scientist/Data Scientist (23andMe)
Jasmine Hughes- Data Scientist (InsightRX)
Yiran Chen- Data Scientist (Abbot)
Chung Yan Cheung - Data Scientist (Rally Health)
Rebecca Clarkson- Software Engineer (Apple)
Claire Tang - Data Scientist (SambaTV)
Kesshi Jordan- Data Scientist (Rally Health)
Jessie Lee- Data Scientist (Lyra Health)
Emily Anderson- Data Scientist (Clover Health)
John Nicoludis - Data Scientist (Invitae)
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Join the community! Learn the domain! Build your network! Get a job!
Questions?Careers in Data, making the transition, or
Insight?