TMLS2017: Transitioning to Data Science, Panel Discussion
52Part of Toronto Machine Learning Summit 2017. The panelists discussed the nuances of "becoming a data scientists" and also "how businesses should create data science teams".
Hosted by: Amir Feizpour (http://amirfeizpour.pythonanywhere.com/)
also see: https://www.linkedin.com/pulse/becoming-data-scientist-amir-feizpour/
Panelists:
Baiju Devani (https://www.linkedin.com/in/bdevani/)
Ozge Yeloglu (https://www.linkedin.com/in/ozgeyeloglu)
Amir Hajian (https://www.linkedin.com/in/amir-hajian-744674135/)
Lindsay Farber (https://www.linkedin.com/in/lindsayefarber)
Questions:
4:20
1) What is your definition of data scientist (vs data engineer vs data/business analyst vs other IT jobs, etc.)?
9:45
2) Is it all just hype? Is data science actually impacting businesses, or is it all hype? Do big companies have real data science problems, or are their problems mostly data engineering or governance and legacy problems?
17:30
3) How do you make a good data science team?
23:16
4) How does data science benefit from diversity?
How important is having a PhD?
Is there any difference between data scientists with academic backgrounds vs business backgrounds?
Is gender and cultural diversity important?
30:10
5) What is your favorite interview question when hiring a data scientist, and why? What is the top tech skill you look for in a data scientist? The top soft skill?
37:50
6) How should candidates fill the gap for lack of prior business experience?
44:18
7) What is the most important question candidates should ask when considering a new role?
51:20
8) What is the single most valuable fact that you wish the candidates you interview knew?
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