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
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
4. WHAT DO YOU WANT • Stimulating environment • Great team • Space to experiment and to grow • Develop yourself • Learn new things • Salary (?) • <Insert more here>
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
6. THREE KIND OF COMPANIES • Heavy R&D department • Tech company, software driven, internet first (only) • All the others • Speak about the latter (majority)
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
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
9. WHAT DO COMPANIES ACTUALLY NEED • Put things into production • They don’t teach that in any data science MOOC (that I know)
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
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
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
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
14. WHAT THESE COMPANIES EXPECT FROM YOU • Good software (code + non functional) • Monitor your models
15. GOOD SOFTWARE? • Testable (and tested) • Modular (otherwise you cannot test it) • DRY • Efficient • Performant • Maintainable (clear code!)
16. INTERMEZZO: BEST SOFTWARE • Demo here! Some real Python code!
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
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!
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?
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
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)
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
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)
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
25. QUESTIONS? • We’re hiring • Data scientists & Machine Learning Engineers! • career@godatadriven.com