Getting a job in data science

The 21th century sexiest job sure seems... sexy. Data scientists are more in demand than ever and it's hard to find a cv of a newcomer that doesn't say Kaggle or Machine Learning (Udacity/Coursera/Insert your own MOOC here). But what do companies really need and how can you exceed their need? A hand wavy introduction to the job market in applied data science (in the Dutch landscape)

Transcript

1. SO YOU WANT TO WORK IN DATA
   SCIENCE
   Giovanni Lanzani
   @gglanzani
   

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2. WHO AM I
   • Imported from Italy ca. 2006
   • Master & PhD in Theoretical Physics in Leiden (2006-2012)
   • Consultant Software Quality @ KPMG (2012-2013)
   • Data Whisperer @ GoDataDriven (2013-2016)
   • Chief Science Officer @ GoDataDriven (2016-…)
   • Father of 5
   

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4. WHAT DO YOU WANT
   • Stimulating environment
   • Great team
   • Space to experiment and to grow
   • Develop yourself
   • Learn new things
   • Salary (?)
   •
   

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5. WHAT COMPANIES (THOUGHT THEY)
   WANTED
   • All the things big data
   • Predictive modeling & Advanced Analytics
   • Make moar money
   • Do all the cool things the others are doing
   

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6. THREE KIND OF COMPANIES
   • Heavy R&D department
   • Tech company, software driven, internet first (only)
   • All the others
   • Speak about the latter (majority)
   

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7. WHAT COMPANIES GOT
   • A lot of POCs
   • A lot of screenshots/presentations/dashboards on a laptop
   • Extra mouths to feed with no returns
   • Nice stories to tell to their network, about those screenshots and especially
   those dashboards
   • Headaches with data and infra even more scattered
   

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8. BUT…
   • We got a data scientist working on trees, and forests
   • Neural networks
   • Deeply convoluted neural networks
   • Deep learning!
   • All the above, and more, is taught in popular MOOCs
   

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9. WHAT DO COMPANIES ACTUALLY NEED
   • Put things into production
   • They don’t teach that in any data science MOOC (that I know)
   

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10. JOB MARKET 2016: US
    • Ask HN: What's the state of the job market in data science and machine learning?
    • https://news.ycombinator.com/item?id=13232883
    • The supply-demand dynamics have changed a lot in the last couple years.
    • Two groups: people with work experience + strong software development skills, and those without
    • The first group is in higher demand than ever
    • The second group has gotten extremely crowded [from people] […] who have completed MOOCs or
    bootcamps
    • Supply keeps growing while demand is flat or shrinking
    • especially as executives get burned by “data scientists” who don't know how to help them build
    things of value
    

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11. JOB MARKET 2016: US
    • The biggest differentiator I've seen is to be able to participate in actually building
    production quality systems vs being proficient enough in R or python to hack
    together a prototype on a very small dataset
    

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12. JOB MARKET 2017: NL
    • I am seeing the same things happening
    • We (GoDataDriven) are definitely only interested in these profiles (people
    who are already there, or that are getting there)
    • Many of our clients are in the same position
    

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13. WHICH COMPANIES CAN REALLY DO
    APPLIED DATA SCIENCE
    These are the companies you should be aiming to work for!
    • Business case for TP, TN & Cost of FP and FN
    • Data {insert something here} should be pro grade
    

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14. WHAT THESE COMPANIES EXPECT FROM
    YOU
    • Good software (code + non functional)
    • Monitor your models
    

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15. GOOD SOFTWARE?
    • Testable (and tested)
    • Modular (otherwise you cannot test it)
    • DRY
    • Efficient
    • Performant
    • Maintainable (clear code!)
    

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16. INTERMEZZO: BEST SOFTWARE
    • Demo here! Some real Python code!
    

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17. I’VE TOLD YOU SO
    • https://blog.godatadriven.com/production-ready-ds
    • Many data scientists approach the problem at hand with a Kaggle-like mentality:
    delivering the best model in absolute terms, no matter what the practical implications
    are.
    • In reality it's not the best model that we implement, but the one that combines
    quality and practicality.
    • Netflix competition
    

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18. BUT WAIT, I DON’T WANT TO DO THAT
    • There is a simple solution to this: companies should hire Machine Learning
    Engineers: help the data scientists productionizing ML/DS
    • The role currently doesn’t (really) exist
    • That means (almost) nobody has them!
    

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19. BUT WAIT, I DON’T WANT TO DO THAT
    • Intermezzo
    • What’s your experience
    • You can tell me how (much) I’m wrong
    • Are you hiring/are you searching?
    

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20. OK, HOW CAN I LEARN THAT
    • Code, code, code
    • If already in industry, start project/working with developers
    • OS contributions
    • Mini sales pitch, you can leave for beer already!
    • GoDataDriven offers the data science accelerator program
    • 12 + 12 (or 5 + 5) modules, with lecture + hands-on day
    • Hands-on day helps you put in practice how you can really use what you learn
    • Also productionizing your code!
    • /salespitch
    

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21. OK, BUT I REALLY CAN’T LEARN THAT
    • Don’t despair
    • Find your niche (finance, biology, marine, energy, etc.)
    • Find a voice
    • Explorative Data Analysis with Story Telling
    • Convincing stakeholders is still one of the most important skills (not only DS)
    

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22. TECH COMPANY, SOFTWARE DRIVEN,
    INTERNET FIRST
    • Where there’s a mature, professional, software engineering culture, the DS
    don’t have to worry about all the above (still about a lot though)
    • But they still have to code to be understood
    • No SE will save you from being lazy about good code
    • Booking.com, Bol.com, Marktplaats/eBay
    • Probably many others as well
    

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23. HEAVY R&D
    • There the focus is much more on research
    • Productionizing is in the (distant) future
    • Domain expertise is (often) more important than ML/DS
    • People working there have the chops to learn ML/DS
    • (Hint: They don’t always do it properly)
    

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24. FINAL NOTE
    • If you have no previous experience, you won’t likely be called every week with
    job offers
    • You might land a data science job, but instead of doing ML, you might end up
    doing ETL, glue code, write SQL, etc)
    • Hang in there: learn the skills to get where you want
    • After the first 1-2 y of experience, it’s usually downhill
    

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25. QUESTIONS?
    • We’re hiring
    • Data scientists & Machine Learning Engineers!
    • career@godatadriven.com
    

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