About the Book
It's quite possible that the only thing more confusing than defining data science is actually hiring data scientists. Hiring Data Scientists and Machine Learning Engineers is a concise, practical guide to cut through the confusion. Whether you're the founder of a brand new startup, the senior vice president in charge of "digital transformation" at a global industrial company, the leader of a new analytics effort at a non-profit, or a junior manager of a machine learning team at a tech giant, this book will help walk you through the important questions you need to answer to determine what role and which skills you should hire for, how to source applicants, how to assess those applicants' skills, and how to set your new hires up for success. Special emphasis is placed on in-office vs remote hiring situations.
Among other things, Hiring Data Scientists and Machine Learning Engineers will help you
- Nail down your hiring needs
- Write an effective job description
- Work effectively with HR to source talent
- Efficiently filter applicants
- Conduct high signal interviews
- Optimize your process for remote or on-site hires
- Smell out DS BS
Additionally, there are interviews throughout the book with experienced DS and MLE hiring managers lending their perspectives on the difficulties in hiring and effective strategies to hire the best teams. Including interviews with
- Julie Hollek, Senior Data Science Manager at Mozilla
- Chris Albon, Director of Machine Learning at The Wikimedia Foundation
- Sean Taylor, Data Science Manager at Lyft
- Angela Bassa, Senior Director of the Data Science and Analytics Center of Excellence at iRobot
- Ravi Mody, Engineering Manager at Spotify
An analytics and machine learning team is only as good as the people you hire. Let Hiring Data Scientists and Machine Learning Engineers guide you to your best possible team.
About the Author
Roy Keyes has worked in data science since 2012, building and leading teams at multiple tech startups and consulting for clients across a wide range of industries. Prior to data science, he received a PhD in computational physics, focusing on medical applications.