178: Data Science and Sponsorship with Emily Robinson
April 15th, 2020 · 59 mins 49 secs
About this Episode
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02:30 - Emily’s Superpower: Finding all the dogs to pet.
05:05 - Emily’s Data Science Journey
- Organization Behavior
- Qualitative / Quantitative
- Research: Women in STEM Fields
08:21 - The Idea of Passion
- Unlocking the Clubhouse: Women in Computing
10:46 - Defining Data Science
- Decision Science
- Machine Learning
13:48 - Emily’s Book: Build a Career in Data Science
16:11 - Dealing with Failure
18:30 - Sponsorship
- Emily’s Post on Sponsorship: The Importance of Sponsorship
20:08 - The Spread of Data Science Roles
- Strengthening Job-Critical Skills
27:09 - Sponsorship (Cont’d)
- Sponsorship vs Mentorship
- Having a Solid Community / Network
- Smaller Acts of Mentorship
- Sponsorship and Mentorship Work Best When There's a Concrete, Stated Goal
- Mentorship Should Be Part of Our Formal Career Ladder
36:20 - Themes Learned From Writing Build a Career in Data Science
- Communication Skills
- Community (Network is Important)
40:02 - Companies Should Train People to be Mentors
43:07 - Measuring Productivity
- Thinking Fast and Slow
- How to Measure Anything
- The Tyranny of Metrics
- Engineering Career Development at Etsy
45:48 - External vs Internal Data Science
- People Scientists
47:18 - Women and Diverse Representation in Data Science
- Groups & Resources
- Why Women Are Flourishing In R Community But Lagging In Python
Avdi: Mentorship does not have to be a huge commitment to be useful and sponsorship is often as important -- or more important than mentorship.
Chanté: What are the things that we’re willing to do for people who need an extra boost or push or support?
Jacob: Mentorship is possible without the mentors knowing they’re even doing it.
Rein: If you’re a mentor and your mentees aren’t coming to you with well-formed questions, it’s your job to coach them into that as a mentor.
Emily: There aren’t enough resources for senior engineers on the non-technical side of things.
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