Subject archive for "data-science-leaders," page 6

Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May 6th, on KQED and local affiliates. Members of Domino were in the live audience for the City Arts talk. This Domino Data Science Field Note provides insights excerpted from Crawford’s City Arts talk and from her NIPS keynote for additional breadth, depth and context for our data science blog readers. This blog post covers Crawford’s research that includes bias as a socio-technical challenge, implications when systems are trained on and ingest biased data, model interpretability, and recommendations for addressing bias.

Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and interactive breakouts. The learnings, anecdotes, and best practices shared in the workshop were based upon years of candid discussions with customers about managing and accelerating data science work. The workshop also featured reusable templates that included a pre-flight data science project checklist as well as a planning template for hiring and onboarding data scientists. We are sharing the breakout materials based on attendee feedback. If you missed Strata and are interested in joining similar discussions, then consider attending Rev.

In this post, Hoda provides insight into how companies with a growing data science capability can structure their organization to ensure that data science provides them with a competitive advantage.

In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next.

We recently published a practical guide for data science management intended to help current and aspiring managers learn from the challenges and successes of industry leaders. This blog post provides a distilled summary of the guide.

Nick Kolegraff is the founder of Whiteboard, a strategic innovation company focused on machine learning and AI. Previously, Kolegraff was the Chief Data Scientist at Rackspace and a Principal Data Scientist at Accenture. As a part of Domino’s “Data Science Leaders at Work” guest blogger series, Kolegraff provides advice for data scientists and data science managers to consider when, or if, they decide to take a “chief data scientist” role. This advice includes insights on the mindset you need to have, the types of problems you need to solve, and the people you need to hire. There are three posts in total. This third post focuses on the people you need to hire.