Building a Data Science Team

Paris Machine Learning Meetup - 12/11/2014

Transcript

1. Building a Data Science Team What NLP and Machine Learning can teach us #MLParis - @chris_bour Paris Machine Learning Applications Group – November 12, 2014 – Paris, France Christophe Bourguignat – AXA Data Innovation Lab
2. Building a Data Science Team Option 1 : hire an army of Super Heroes
3. « A Great Data Science Team Is Like A Jazz Quartet » Harvard Business Review
4. « A Great Data Science Team Is Like A Jazz Quartet » Harvard Business Review The machine learning god
5. « A Great Data Science Team Is Like A Jazz Quartet » Harvard Business Review The software development hero The machine learning god
6. « A Great Data Science Team Is Like A Jazz Quartet » Harvard Business Review The software development hero The machine learning god The IT infrastructure guru
7. « A Great Data Science Team Is Like A Jazz Quartet » Harvard Business Review The software development hero The machine learning god The data business rock star The IT infrastructure guru
8. Idea • Collect several profiles on Linkedin • Manually label each profile among 4 (unordered !) classes : • The machine learning god • The software development hero • The IT infrastructure guru • The data business rock star • Do NLP (Natural Langage Processing) and ML (Machine Learning)
9. IPython Notebook Download : https://github.com/christophebourguignat
12. Machine Learning God Software Development Hero IT Infrastructure Guru Data Business Rock Star Python Algorithm Software Data Analytics Data Science Management Customer Solution Project Development Architecture Machine Learning Statistical Big Data Value User Integration Java Application
15. Lessons learned • With a quite simple model, it’s possible to cluster the 4 profiles • Typical terms for each profile are aligned with our intuition • “IT architecture” only, or “Business” only profiles are uncommon. They are often mixed with an other profile • Some profiles are difficult to discriminate, as they are in-between (also in line with intuition) : • IT architecture and software development • IT architecture and business
16. Improvement suggestions • Use much more Linkedin profiles to train the model ! • Cross-validate to find the best model and its meta-parameters • Make an API to allow anybody to try with its own profile
17. The Sexiest Job Of The 21th Century
18. Twitter : @chris_bour Blog : https://medium.com/@chris_bour Notebooks : https://github.com/christophebourguignat Decks : https://speakerdeck.com/kriss THANK YOU