As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process.

Having served as principal data scientist in several companies, Chang has considerable experience as both ML interviewer and interviewee. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way. You'll quickly understand how to successfully navigate your way through typical ML interviews.

This guide shows you how to:

• Explore various machine learning roles, including ML engineer, applied scientist, data scientist, and other positions
• Assess your interests and skills before deciding which ML role(s) to pursue
• Evaluate your current skills and close any gaps that may prevent you from succeeding in the interview process
• Acquire the skill set necessary for each machine learning role
• Ace ML interview topics, including coding assessments, statistics and machine learning theory, and behavioral questions
• Prepare for interviews in statistics and machine learning theory by studying common interview questions

Table of Contents

Chapter 1. Machine Learning Roles and the Interview Process

Chapter 2. Machine Learning Job Application and Resume

Chapter 3. Technical Interview: Machine Learning Algorithms

Chapter 4. Technical Interview: Model Training and Evaluation

Chapter 5. Technical Interview: Coding

Chapter 6. Technical Interview: Model Deployment and End-to-End ML

Chapter 7. Behavioral Interviews

Chapter 8. Tying It All Together: Your Interview Roadmap

Chapter 9. Post-Interview and Follow-up

Who This Book Is For

The following outlines scenarios that you might find relatable; this is the audience I’ve written this book for:

• You are a recent graduate who is eager to become an ML/AI practitioner in industry.
• You are a software engineer, data analyst, or other tech/data professional who is transitioning into a role that focuses on ML day to day.
• You are a professional with experience in another field who is interested in transitioning into the ML field.
• You are an experienced data scientist or ML practitioner who is returning to the interviewing fray and aiming for a different role or an increased title and responsibility, and you would like a comprehensive refresher of ML material.

You could also benefit from this book if the following scenarios describe you:

• Managers who want to get inspiration for how to conduct their ML interviews or nontechnical people who want to get an overview of the process without wasting too much time on scattered online resources.
• Readers who have a basic knowledge of Python programming and ML theory and are curious to explore if entering the ML field could be a future career choice.

What This Book Is Not

• This book is not a statistics or ML textbook.
• This book is not a coding textbook or tutorial book.
• While there are sample interview questions, this book is not a question bank. Code snippets will be brief and concise since they become outdated quickly.

Since I can’t cover every concept from scratch, I assume that readers have a rudimentary familiarity with ML (a high-level understanding is enough). But don’t worry, as I will cover the basic definitions as a quick reminder. I also assume the audience has some familiarity with the Python programming language, such as running scripts on Jupyter Notebooks, since Python is popular in ML interviews and on the job. However, I do include a brief section on learning Python from scratch if you happen to not be familiar with it.

In addition, this book provides a substantial library of links to external practice resources to help you with preparing for ML interviews; but first, I’ll help you identify what is most helpful for you to practice and learn beyond your current knowledge and skill level.

Thus, instead of listing a bunch of questions and answers to memorize, with this book I’m aiming to teach you how to fish. As an interviewer, many candidates I’ve seen who didn’t pass the interview wouldn’t have been saved if they had just practiced some more questions. Rather, they didn’t even know what their gaps were. I’ll teach you how to identify your strengths and gaps and how exactly you can use the resources in this book to close those gaps.

About the Author

Susan Shu Chang is a principal data scientist at Elastic (of Elasticsearch), with previous ML experience in fintech, telecommunications, and social platforms. She’s an international speaker, having given talks at six PyCons worldwide and keynotes at Data Day Texas, PyCon DE & PyData Berlin, and O’Reilly’s AI Superstream. She writes about machine learning career growth in her newsletter, susanshu.substack.com.