Defining the Analytics Profession and Cutting Out the Hype and Confusion
In the prior article, we gave an overview of this panel. This article is to share with you the proposed questions that I plan to ask of our distinguished panelists.
Please feel free to provide comments and suggested additional questions. We will be assembling at 1:30PM London time Wednesday August 22, 2018 at London ExCel – ICC Capital Suite Room 8+11 (Level 3) as part of KDD-2018
Suggested questions/topics for each of the panelists:
- For each of the Panelists, in not more than 1 minute please, if you can say who you are, were you work, what is the relevance of the panel topic to you, and
a. what do you want the audience to get out of this discussion?
b. What do you want to get out of the panel for yourself and your
organization?
c. What would you like to hear or get out of our audience?
2. To Hamit Hamutcu: you have been conducting a study of the various titles people are using in places like LikedIn and various job boards, what can you share about what you saw in the space? Is there a problem? What is it?
3. To each of the panelists, in 1 minute or less:
a. How do you define a data scientist?
b. How does that differ from a data analyst?
c. How does that differ from a statistician?
d. What about a machine learning person? What about a data engineer?
4. To Narendra Mulani: You represent a large employer who has to hire “data scientists” whose job will be to help you large clients solve their data science needs.
a. What are the pains you face?
b. What would you need from the market/organizations like the SIGKDD to
help you?
c. What is your reaction to graduates from academic programs?
5. To Kjersten Moody,: you ran groups of analysts and data scientists in London (Unilever) and now in the U.S. (State Farm – the largest US Insurance company)
a. Are there differences between UK/EU and U.S. markets for talent?
Easier/harder?
b. What skills do you think a good data analyst should have versus a
data scientist?
6. To Jeannette Wing: What do you think is the role of academia here? How much of Data Science is a “science” to be taught vs. a craft to be practiced?
a. Do you feel you understand what the employers need?
b. What do you say to an undersgraduate on a career as a data scientist?
Or does this need Grad school?
7. To Claudia Perlich: How do the needs translate for a company like Two Sigma that is fast growing? What Data Science talent do you need? How do you find it?
8. To Ravi Kumar,: What does Google look for in a data scientist? Do you hire fresh grads and train them? Do you hire experienced data scientists from other companies? Is a Data Scientist in Research/algorithms useful in solving real practical applications?
9. To Dragomir Yankov: How does this differ for Microsoft? What do you look for in a data scientist? Would a good data scientist from Google be a good hire? How about from the big consulting firms? How about fresh grads?
10. To Jeannette Wing: for academia, I think we figured out how to train programmers and engineers. Have we figured out what we need to teach data scientists yet?
11. To the rest of you who are all employers (Narendra Mulani, Kjersten Moody, Claudia Perlich, Ravi Kumar, Dragomir Yankov) – What are your thoughts on fresh grads from academia
12. To Hamit Hamutcu: How does this reconcile with your observations/experience? Where do you think are the gaps in what you heard? How would we address this problem?
13. To each of the panelists: Please share in one sentence, the best advice you can give the audience on Data Science talent preparation and hiring
14. To each of the panelists: Please share in one sentence, the last word of advice/wisdom you would like to share with the audience