Hiring Data Scientists and Machine Learning Engineers
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Hiring Data Scientists and Machine Learning Engineers

A Practical Guide

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.

The paperback version is available at The Book Patch and Amazon.

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  • Categories

    • Artificial Intelligence
    • Management
    • Data Science
    • Engineering Management
    • Machine Learning
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About the Author

Roy Keyes
Roy Keyes

Roy Keyes has worked in data science since 2012, building and leading teams at multiple tech startups as well as consulting for clients across a wide range of industries. Prior to data science, he received a PhD in computational physics, focusing on medical applications.

You can find his website and blog at roycoding.com.

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Reader Testimonials

Vicki Boykis
Vicki Boykis

MLE at Tumblr/Automattic, Newsletter Maestro of Normcore

Hiring is still such a weird, imprecise science, and paired even more with the uncertainty of what a DS/MLE role actually entails. This is a fantastic, even-keeled book full of good recommendations (I bought and pre-read), and I think it's helpful both to managers and candidates.

Matthew Martz
Matthew Martz

Principal, Research and Machine Learning at PenroseHill/Firstleaf

This is a fantastic read. Whether hiring or looking to be hired, you should read Roy's book!


Thank you for such an amazing resource.

Table of Contents

  • Acknowledgements
  • Chapter 1: Introduction
    • What is this book?
    • Who is this book for?
    • Why is this book needed?
    • Why is this book any good?
  • Chapter 2: What is data science and machine learning?
    • What is data science and who is a data scientist?
    • What is machine learning and machine learning engineering?
    • Wrapping up
  • Interview: Julie Hollek, Mozilla
  • Chapter 3: Determining what roles you need
    • Defining your business goal
    • How do data science and machine learning fit in?
    • Defining the roles you need
    • The many roles in data science
    • Determining the roles you need
    • Determining job titles for new roles
    • Wrapping up: Roles
  • Interview: Chris Albon, Wikimedia
  • Chapter 4: Creating a hiring strategy
    • Determining your resources and constraints
    • Understanding the job market
    • Scaling to meet your expected applicant volume
    • Determining your recruiting strategy
    • Structuring your hiring process
    • Making decisions
    • Summary
  • Interview: Sean Taylor, Lyft
  • Chapter 5: Job descriptions and resumes
    • Writing an effective job description
    • Reviewing resumes
    • Summary
  • Interview: Angela Bassa, iRobot
  • Chapter 6: Technical skills assessment
    • Skills assessment
    • What does it mean to assess technical skills?
    • How can you assess technical skills?
    • What are we trying to assess?
    • Methods for skills assessment
    • Creating a fair skills assessment
    • Deciding on your strategy
    • Which skills should you be assessing?
    • Choosing assessment material
    • Assessing your assessment material
    • Scoring and decision making
    • The life cycle of your assessments
    • Considerations for “live” skills assessments
    • Summary
  • Interview: Ravi Mody, Spotify
  • Chapter 7: Interviewing
    • The goals of the interview process
    • When to do which interviews
    • What should you be assessing during an interview?
    • Interviewing strategy
    • Interviewing logistics and decision making
    • How do you know if your hiring process is succeeding?
    • Summary
  • Chapter 8: Setting up your team for success
    • Structuring your team
    • Onboarding new hires
    • Care and feeding of data scientists and MLE’s
    • Summary
  • Appendices
    • Appendix I: Task and skills breakdowns, with associated roles
    • Appendix II: Skills of different roles
  • Summary and Cheat Sheet
    • Steps to effective DS/MLE hiring
  • Notes

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