Becoming a Data Scientist through Non-Traditional Paths

On August 12, 2020, Women in Big Data Wisconsin Chapter hosted a live panel discussion on Becoming a Data Scientist through Non-Traditional Paths. The panelists shared their experiences on how they became Data Scientists from various educational backgrounds. They provided recommendations on how someone with no former education could build up his/her portfolio and capture employers’ attention.

Q&A included:

  • What sets professional Data Scientists apart from those that are not true Data Scientists? What distinguishes a data scientist from someone that works with big data?
  • How much software engineering should an entry level data scientist be comfortable with?
  • Are there any tools that you use, or your teams use?
  • Are data scientists responsible for developing their own theories or solving specific data problems? How autonomous is a data scientist’s job?
  • Do AI, ML or DS mean different things to you?
  • Where do you see this field going in the next 5 years?

Panelists:

Devin Conathan
Devin joined the machine learning research team at American Family Insurance as an intern in the summer of 2016 and came on full-time the following year. He has undergraduate degrees in mathematics and philosophy from Cornell University and a masters in electrical engineering with a focus on optimization and active learning research from the University of Wisconsin-Madison. Currently, he is developing a research knowledge management platform based on knowledge graphs and state-of-the-art natural language processing techniques.

Mary Willcock
Coming from a non-traditional background, Mary worked in the financial industry for ten years prior to continuing her education at Northwestern University with a Master’s degree in Data Science specializing in Analytics and Modeling. As a Data Scientist, Mary has worked in the Education Technology and Retail industries and now as a consultant at Baker Tilly. Mary is gaining experience in a wide variety of industries and projects.

Michael Sutherland
Michael is currently a Data Scientist with Acoustic, where he provides modeling services for the DemandTec LifeCycle Pricing Solution. He has undergraduate degrees in Physics and Astronomy, and a Ph.D. in Physics from the Ohio State University. He has worked on a number of astrophysics experiments, including the Pierre Auger, ANITA, and IceCube Observatories searching for the highest energy particles in the universe. During that time, he developed skills readily transferable to data science activities in retail business.

Click here to view the presentation.

By Dorothy Zheng

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