Datacast

Episode 69: DataPrepOps, Active Learning, and Team Management with Jennifer Prendki


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Show Notes
  • (01:46) Jennifer shared her formative experiences growing up in France and wanting to be a physicist.
  • (03:04) Jennifer unpacked the evolution of her academic journey in France — getting Physics degrees at Louis Pasteur University, Paris-Sud University, and Sorbonne University.
  • (06:44) Jennifer mentioned her time as a Postdoctoral Researcher in Neutrino Physics at Duke University, where her research group lacked the funding to carry on scientific projects.
  • (09:35) Jennifer discussed her transition from academia to industry, working as a Quantitative Research Scientist at Quantlab Financial in Houston.
  • (13:31) Jennifer went over her move to the Bay Area, working for YuMe and Ayasdi — growing and managing early-stage data science teams at both places.
  • (19:19) Jennifer recalled her foray into becoming a Senior Data Science Manager of the Search team at Walmart Labs. She managed the Metrics-Measurements-Insights team and the Store-Search team.
  • (23:59) Jennifer shared the business anecdote that made her obsessed with measuring the ROI of data science.
  • (28:46) Jennifer reflected on the opportunity to give conference talks and become a thought leader in the data science community (watch her first industry talk, “Review Analysis: An Approach to Leveraging User-Generated Content in the Context of Retail” at MLconf 2016).
  • (31:10) Jennifer unpacked her interest in active learning and outlined existing challenges of making active learning performant in real-world ML systems.
  • (36:58) After 1.5 years with Walmart Labs, Jennifer became the Chief Data Scientist at Atlassian. She shared the tactics to grow the Search & Smarts team of scientists and engineers from 3 to 17 people in less than 6 months across 3 locations.
  • (40:31) Jennifer discussed the organizational and operational challenges with making ML useful in enterprises and the importance of data preparation in the modern ML stack.
  • (47:24) Jennifer elaborated on the topic of “Agile for Data Science Teams,” which discusses that organizations that invest in ML but do not get the organizational side of things right will fail.
  • (53:09) Jennifer went over her decision to accept a VP of Machine Learning role at Figure Eight, then a frontier startup that offers enterprise-grade labeling solutions to ML teams.
  • (57:56) Jennifer went over the inception of her startup Alectio, whose mission is to help companies do ML more efficiently with fewer data and help the world do ML more sustainably by reducing the industry’s carbon footprint.
  • (01:04:32) Jennifer unpacked her 4-part blog series about responsible AI that calls out the need to fight bias, increase accessibility, and create more opportunities in AI.
  • (01:09:06) Jennifer discussed the hurdles she had to jump through to find early adopters of Alectio.
  • (01:11:03) Jennifer emphasized the valuable lessons learned to attract the right people who are excited about Alectio’s mission.
  • (01:14:38) Jennifer cautioned the danger of taking advice without thinking through how it can be applied to one’s career.
  • (01:17:09) Jennifer condensed her decade of experience navigating the tech industry as a woman into concrete advice.
  • (01:19:19) Closing segment.
Jennifer’s Contact Info
  • LinkedIn
  • Twitter
  • Medium
Alectio’s Resources
  • Website
  • Twitter
  • LinkedIn
  • What Is Alectio? (Video)
  • Is Big Data Dragging Us Towards Another AI Winter? (Article)
Mentioned ContentTalks
  • The Day Big Data Died (Oct 2020 @ Interop Digital)
  • The Importance of Ethics in Data Science (Keynote @ Women in Analytics Conference 2019)
  • Introduction to Active Learning (ODSC London 2018)
  • Agile for Data Science Teams (Strata Data Conf — New York 2018)
  • Big Data and the Advent of Data Mixology (Interop ITX — The Future of Data Summit 2017)
  • The Limitations of Big Data In Predictive Analytics (DataEngConf SF 2017)
  • Review Analysis: An Approach to Leveraging User-Generated Content in the Context of Retail (MLconf 2016)
Articles

1 — Women vs. The Workplace Series

  • Gender Discrimination (Oct 2015)
  • Why Leading By Example Matters (Jan 2017)
  • Data Scientist: the SexISTiest Job of the 21st Century? (Feb 2017)
  • The Role of Motherhood in Gender Discrimination (March 2017)
  • The Biggest Challenges of the Female Manager (May 2017)
  • Parity in the Workplace: Why We Are Not There Yet (Dec 2017)
  • The Pyramid of Needs of Professional Women (Dec 2017)

2 — Management Series

  • The Secrets to Successfully Managing an Underperformer (June 2017)
  • The Top Secrets to Managing a Rockstar (July 2017)
  • The Real Cost of Hiring Over-Qualified Candidates in Technology (March 2018)
  • Team Culture (May 2018)

3 — Responsible AI Series

  • How We Got Responsible AI All Wrong (Part 1)
  • Impact, Bias, and Sustainability in AI (Part 2)
  • Increasing Accessibility to AI (Part 3)
  • Creating More Opportunities in AI (Part 4)
Book
  • “Managing Up” (by Rosanne Badowski and Roger Gittines)
Notes

Jennifer told me that Alectio is about to launch a community version that people will be able to compete to get the best model with the minimum amount of data this fall. Be sure to check out their blog and follow them on LinkedIn!

About the show

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing [email protected].

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DatacastBy James Le