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About
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.
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.
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
A key takeaway
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
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