Advice from a panel

I recently attended a panel discussion of four data scientists who are former particle physicists:

Eric Flattum
Distinguished Member of Technical Staff at Verizon
Michigan State University, PhD Physics (D0 experiment)

Kathy Copic
Director of New Initiatives and Growth, Insight Data Science
University of Michigan, PhD Physics (CDF and ATLAS experiments)

Heather Gerberich
Data Scientist, On Point Technology, Inc.
Duke University, PhD Physics (CDF experiment)

John Mansour
Vice President Advanced Solutions Group, Nielsen
University of Rochester, PhD Physics (E706 and CDF experiments)

Here is some of their advice that I found useful.

1. "Data science" is a very vague term, and it refers to many different kinds of jobs. For each opportunity, find out exactly what you would be doing, and choose carefully. Jobs like "fit this Gaussian" and "look up something in this Excel spreadsheet" are going to disappear soon. Once you get into a company, you'll have room to move around, doing a wider range of projects or taking on management responsibilities.
2. Keep track of what you're doing daily as a student or postdoc, and always think about where you could use those skills. We forget sometimes that we know how to do useful things, and you're going to have to sell your skills.
3. Put together a portfolio of small projects. Emphasis on small. These can't be Coursera or class projects, since everyone looking for data science jobs have done these, and you need to differentiate yourself. The point is to provide evidence that you can work on real data to solve real problems. Try to pick something that's important to you. A panelst gave an example of a man who made an app to find hiking trails within some distance of his house by crawling the web. 
4. In industry, there is a stereotype of PhDs that makes employers nervous about hiring them: that they like to work alone on difficult projects for a long time. Show them that you are willing to collaborate, and compromise between perfect and fast.
5. Physics PhDs have three skills that employers like. Finding these skills in one person is apparently difficult.

• Knowledge of analytics: a working understanding of basic techniques in machine learning, for example. 
• Software development: the basic ability to import, process, and display data. 
• Problem analysis: knowing when the data don't make sense, or something went wrong. Being able to estimate the most promising methods.

Something that I took home from this discussion was that my bar for saying that I understand something (say, Python) is waaaaaay too high. If you are comfortable answering questions about it in an interview, it can go on your resume.

- b