,Chapter 1. Machine Learning Roles and the
Interview Process
In the first part of this chapter, I’ll walk through the structure of this book. Then, I’ll discuss the
various job titles and roles that use ML skills in industry.1 I’ll also clarify the responsibilities of
various job titles, such as data scientist, machine learning engineer, and so on, as this is a common
point of confusion for job seekers. These will be illustrated with an ML skills matrix and ML
lifecycle that will be referenced throughout the book.
The second part of this chapter walks through the interview process, from beginning to end. I’ve
mentored candidates who appreciated this overview since online resources often focus on specific
pieces of the interview but not how they all connect together and result in an offer. Especially for
new graduates2 and readers coming from different industries, this chapter helps get everyone on
the same page as well as clarifies the process.
The interconnecting pieces of interviews are complex, with many types of combinations depending
on the ML role you’re aiming for. This overview will help set the stage, so you’ll know what to
focus your time on. For example, some online resources focus on knowledge specific to “product
data scientists,” but will title the course or article “data scientist interview tips” without
differentiating. For a newcomer, it’s hard to tell if that is relevant to your own career interests.
After this chapter, you’ll be able to tell what skills are required for each job title, and in Chapter 2,
you’ll be able to parse out that information yourself from job postings and make your resume as
relevant to the job title and job posting as possible.
Overview of This Book
This chapter focuses on helping you differentiate among various ML roles, and walks through the
entire interview process, as illustrated in Figure 1-1:
Job applications and resume (Chapter 2)
Technical interviews
Machine learning (Chapters 3, 4, and 6)
Coding/programming (Chapter 5)
Behavioral interviews (Chapter 7)
Your interview roadmap (Chapter 8)
Post-interview and follow-up (Chapter 9)
,Figure 1-1. Overview of the chapters and how they tie into the ML interview process.
Depending on where you are in your ML interview journey, I encourage you to focus on the
chapters and sections that seem relevant to you. I’ve also planned the book to be referenced as you
go along; for example, you might iterate on your resume multiple times and then flip back
to Chapter 2 when needed. The same applies to the other chapters. With that overview, let’s
continue.
TI P
The companion site to this book, https://susanshu.substack.com, features bonus content, helper resources,
and more.
A Brief History of Machine Learning
and Data Science Job Titles
First, let’s walk through a brief history of job titles. I decided to start with this section to dispel
some myths about the “data scientist” job title and shed some light on why there are so many ML-
related job titles. After understanding this history, you should be more aware of what job titles to
, aim for yourself. If you’ve ever been confused about the litany of titles such as machine learning
engineer (MLE), product data scientist, MLOps engineer, and more, this section is for you.
ML techniques aren’t a new thing; in 1985, David Ackley, Geoffrey E. Hinton, and Terrence J.
Sejnowski popularized the Boltzmann Machine algorithm.3 Even before that, regression
techniques4 had early developments in the 1800s. There have long been jobs and roles that use
modeling techniques to forecast and predict. Econometricians, statisticians, financial modelers,
physics modelers, and biochemical modelers have existed as professions for decades. The main
difference is that there were much smaller datasets compared to the modern day (barring
simulations).
It was only in recent years, just before the 21st century, when compute power started to increase
exponentially. In addition, advances in distributed and parallel computing created a cycle in which
“big data” became more readily available. This allowed practitioners to apply that advanced
compute power to millions or billions of data points.
Larger datasets started being accumulated and distributed for ML research, such as WordNet,5 and,
subsequently, ImageNet,6 a project led by Fei-Fei Li. These collective efforts laid the foundation
for even more ML breakthroughs. AlexNet7 was released in 2012, achieving high accuracy in the
ImageNet challenge,8 which demonstrated that deep learning can be adept at humanlike tasks at a
scale that had not been seen before.
Many ML practitioners see this as a time when machine learning, deep learning, and related topics
increased by leaps and bounds in terms of recognition from the broader population, not just the AI
community. The recent popularity of generative AI (such as ChatGPT) in 2022 and 2023 didn’t
come out of nowhere, nor did the deepfakes, self-driving cars, chess bots, and more that came
before it; these applications were the results of many advances over recent years.
“Data scientist” as a job title began as an umbrella term, when the ML and data fields were less
mature. The term “data scientist” on Google Trends, which measures the popularity of search
terms, surged in 2012. That was the year when that article was published by Harvard Business
Review: “Data Scientist: The Sexiest Job of the 21st Century.”9 By April 2013, the search
popularity of “data scientist” was already tied with “statistician” and subsequently surpassed it by
magnitudes, as shown in Figure 1-2. Back in those days, there wasn’t a narrow divide between
infrastructure jobs and model training, though. For example, Kubernetes was first released in 2014,
but companies have taken some time to adopt it for orchestrating ML jobs. So now there are more
specific job titles for ML infrastructure that didn’t exist before.
Interview Process
In the first part of this chapter, I’ll walk through the structure of this book. Then, I’ll discuss the
various job titles and roles that use ML skills in industry.1 I’ll also clarify the responsibilities of
various job titles, such as data scientist, machine learning engineer, and so on, as this is a common
point of confusion for job seekers. These will be illustrated with an ML skills matrix and ML
lifecycle that will be referenced throughout the book.
The second part of this chapter walks through the interview process, from beginning to end. I’ve
mentored candidates who appreciated this overview since online resources often focus on specific
pieces of the interview but not how they all connect together and result in an offer. Especially for
new graduates2 and readers coming from different industries, this chapter helps get everyone on
the same page as well as clarifies the process.
The interconnecting pieces of interviews are complex, with many types of combinations depending
on the ML role you’re aiming for. This overview will help set the stage, so you’ll know what to
focus your time on. For example, some online resources focus on knowledge specific to “product
data scientists,” but will title the course or article “data scientist interview tips” without
differentiating. For a newcomer, it’s hard to tell if that is relevant to your own career interests.
After this chapter, you’ll be able to tell what skills are required for each job title, and in Chapter 2,
you’ll be able to parse out that information yourself from job postings and make your resume as
relevant to the job title and job posting as possible.
Overview of This Book
This chapter focuses on helping you differentiate among various ML roles, and walks through the
entire interview process, as illustrated in Figure 1-1:
Job applications and resume (Chapter 2)
Technical interviews
Machine learning (Chapters 3, 4, and 6)
Coding/programming (Chapter 5)
Behavioral interviews (Chapter 7)
Your interview roadmap (Chapter 8)
Post-interview and follow-up (Chapter 9)
,Figure 1-1. Overview of the chapters and how they tie into the ML interview process.
Depending on where you are in your ML interview journey, I encourage you to focus on the
chapters and sections that seem relevant to you. I’ve also planned the book to be referenced as you
go along; for example, you might iterate on your resume multiple times and then flip back
to Chapter 2 when needed. The same applies to the other chapters. With that overview, let’s
continue.
TI P
The companion site to this book, https://susanshu.substack.com, features bonus content, helper resources,
and more.
A Brief History of Machine Learning
and Data Science Job Titles
First, let’s walk through a brief history of job titles. I decided to start with this section to dispel
some myths about the “data scientist” job title and shed some light on why there are so many ML-
related job titles. After understanding this history, you should be more aware of what job titles to
, aim for yourself. If you’ve ever been confused about the litany of titles such as machine learning
engineer (MLE), product data scientist, MLOps engineer, and more, this section is for you.
ML techniques aren’t a new thing; in 1985, David Ackley, Geoffrey E. Hinton, and Terrence J.
Sejnowski popularized the Boltzmann Machine algorithm.3 Even before that, regression
techniques4 had early developments in the 1800s. There have long been jobs and roles that use
modeling techniques to forecast and predict. Econometricians, statisticians, financial modelers,
physics modelers, and biochemical modelers have existed as professions for decades. The main
difference is that there were much smaller datasets compared to the modern day (barring
simulations).
It was only in recent years, just before the 21st century, when compute power started to increase
exponentially. In addition, advances in distributed and parallel computing created a cycle in which
“big data” became more readily available. This allowed practitioners to apply that advanced
compute power to millions or billions of data points.
Larger datasets started being accumulated and distributed for ML research, such as WordNet,5 and,
subsequently, ImageNet,6 a project led by Fei-Fei Li. These collective efforts laid the foundation
for even more ML breakthroughs. AlexNet7 was released in 2012, achieving high accuracy in the
ImageNet challenge,8 which demonstrated that deep learning can be adept at humanlike tasks at a
scale that had not been seen before.
Many ML practitioners see this as a time when machine learning, deep learning, and related topics
increased by leaps and bounds in terms of recognition from the broader population, not just the AI
community. The recent popularity of generative AI (such as ChatGPT) in 2022 and 2023 didn’t
come out of nowhere, nor did the deepfakes, self-driving cars, chess bots, and more that came
before it; these applications were the results of many advances over recent years.
“Data scientist” as a job title began as an umbrella term, when the ML and data fields were less
mature. The term “data scientist” on Google Trends, which measures the popularity of search
terms, surged in 2012. That was the year when that article was published by Harvard Business
Review: “Data Scientist: The Sexiest Job of the 21st Century.”9 By April 2013, the search
popularity of “data scientist” was already tied with “statistician” and subsequently surpassed it by
magnitudes, as shown in Figure 1-2. Back in those days, there wasn’t a narrow divide between
infrastructure jobs and model training, though. For example, Kubernetes was first released in 2014,
but companies have taken some time to adopt it for orchestrating ML jobs. So now there are more
specific job titles for ML infrastructure that didn’t exist before.
