Subject archive for "managing-data-science," page 2

Perspective

Data Science at The New York Times

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. Many thanks to Chris Wiggins for providing feedback on this post prior to publication.

By Ann Spencer40 min read

Data Science

Themes and Conferences per Pacoid, Episode 10

Co-chair Paco Nathan provides highlights of Rev 2, a data science leaders summit.

By Paco Nathan26 min read

Data Science

Machine Learning Product Management: Lessons Learned

This Domino Data Science Field Note covers Pete Skomoroch’s recent Strata London talk. It focuses on his ML product management insights and lessons learned. If you are interested in hearing more practical insights on ML or AI product management, then consider attending Pete’s upcoming session at Rev.

By Ann Spencer8 min read

Data Science

Themes and Conferences per Pacoid, Episode 9

Paco Nathan's latest article features several emerging threads adjacent to model interpretability.

By Paco Nathan29 min read

Machine Learning

Machine Learning Projects: Challenges and Best Practices

This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. He also provides best practices on how to address these challenges. This post was provided courtesy of Lukas and originally appeared on Medium.

By Lukas Biewald9 min read

Perspective

Growing Data Scientists Into Manager Roles

In this post, Ricky Chachra, Research Science Manager at Lyft, provides insight for companies looking to home-grow their promising individual contributors (ICs) into effective managers. He reflects on his journey at Lyft, where he started as a data/research scientist and transitioned into a science management role. In his new-found capacity, Ricky is helping codify the responsibilities of Science Managers at Lyft. This post is related to a forthcoming companion post by Joshua Nguyen as they compare frameworks born out of their individual experience.

By Ricky Chachra18 min read

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