Machine Learning Interviews: Kickstart Your Machine Learning and Data Career

Cover....1

Copyright....3

Table of Contents....4

Preface....12

Why Machine Learning Jobs?....15

Who This Book Is For....17

What This Book Is Not....17

Conventions Used in This Book....18

O’Reilly Online Learning....19

How to Contact Us....19

Acknowledgments....20

Chapter 1. Machine Learning Roles and the Interview Process....22

Overview of This Book....23

A Brief History of Machine Learning and Data Science Job Titles....24

Job Titles Requiring ML Experience....27

Machine Learning Lifecycle....29

Startups....30

Larger ML Teams....31

The Three Pillars of Machine Learning Roles....33

Machine Learning Algorithms and Data Intuition: Ability to Adapt....33

Programming and Software Engineering: Ability to Build....34

Execution and Communication: Ability to Get Things Done in a Team....34

Clearing Minimum Requirements in the Three ML Pillars....35

Machine Learning Skills Matrix....36

Introduction to ML Job Interviews....38

Machine Learning Job-Interview Process....39

Applying for Jobs Through Websites or Job Boards....40

Resume Screening of Website or Job-Board Applications....41

Applying via a Referral....43

Preinterview Checklist....44

Recruiter Screening....46

Overview of Main Interview Loop....47

Summary....49

Chapter 2. Machine Learning Job Application and Resume....50

Where Are the Jobs?....50

ML Job Application Guide....51

Your Effectiveness per Application....51

Job Referrals....52

Networking....56

Machine Learning Resume Guide....58

Take Inventory of Your Past Experience....58

Overview of Resume Sections....60

Tailoring Your Resume to Your Desired Role(s)....65

Final Resume Touch-ups....69

Applying to Jobs....70

Vetting Job Postings....70

Mapping Your Skills and Experience to the ML Skills Matrix....70

Tracking Applications....72

Additional Job Application Materials, Credentials, and FAQ....73

Do You Need a Project Portfolio?....73

Do Online Certifications Help?....74

FAQ: How Many Pages Should My Resume Be?....77

FAQ: Should I Format My Resume for ATS (Applicant Tracking Systems)?....78

Next Steps....79

Browsing Job Postings....79

Identifying the Gaps Between Your Current Skills and Target Roles....79

Summary....82

Chapter 3. Technical Interview: Machine Learning Algorithms....84

Overview of the Machine Learning Algorithms Technical Interview....84

Statistical and Foundational Techniques....86

Summarizing Independent and Dependent Variables....87

Defining Models....88

Summarizing Linear Regression....89

Defining Training and Test Set Splits....92

Defining Model Underfitting and Overfitting....93

Summarizing Regularization....94

Sample Interview Questions on Foundational Techniques....95

Supervised, Unsupervised, and Reinforcement Learning....97

Defining Labeled Data....98

Summarizing Supervised Learning....99

Defining Unsupervised Learning....99

Summarizing Semisupervised and Self-Supervised Learning....100

Summarizing Reinforcement Learning....102

Sample Interview Questions on Supervised and Unsupervised Learning....102

Natural Language Processing Algorithms....107

Summarizing NLP Underlying Concepts....108

Summarizing Long Short-Term Memory Networks....109

Summarizing Transformer Models....110

Summarizing BERT Models....110

Summarizing GPT Models....112

Going Further....112

Sample Interview Questions on NLP....113

Recommender System Algorithms....116

Summarizing Collaborative Filtering....116

Summarizing Explicit and Implicit Ratings....117

Summarizing Content-Based Recommender Systems....117

User-Based/Item-Based Versus Content-Based Recommender Systems....118

Summarizing Matrix Factorization....118

Sample Interview Questions on Recommender Systems....119

Reinforcement Learning Algorithms....122

Summarizing Reinforcement Learning Agents....123

Summarizing Q-Learning....125

Summarizing Model-Based Versus Model-Free Reinforcement Learning....127

Summarizing Value-Based Versus Policy-Based Reinforcement Learning....128

Summarizing On-Policy Versus Off-Policy Reinforcement Learning....129

Sample Interview Questions on Reinforcement Learning....129

Computer Vision Algorithms....132

Summarizing Common Image Datasets....133

Summarizing Convolutional Neural Networks (CNNs)....134

Summarizing Transfer Learning....135

Summarizing Generative Adversarial Networks....135

Summarizing Additional Computer Vision Use Cases....137

Sample Interview Questions on Image Recognition....139

Summary....140

Chapter 4. Technical Interview: Model Training and Evaluation....142

Defining a Machine Learning Problem....143

Data Preprocessing and Feature Engineering....145

Introduction to Data Acquisition....145

Introduction to Exploratory Data Analysis....146

Introduction to Feature Engineering....147

Sample Interview Questions on Data Preprocessing and Feature Engineering....153

The Model Training Process....154

The Iteration Process in Model Training....154

Defining the ML Task....156

Overview of Model Selection....157

Overview of Model Training....159

Sample Interview Questions on Model Selection and Training....161

Model Evaluation....162

Summary of Common ML Evaluation Metrics....163

Trade-offs in Evaluation Metrics....166

Additional Methods for Offline Evaluation....167

Model Versioning....168

Sample Interview Questions on Model Evaluation....169

Summary....170

Chapter 5. Technical Interview: Coding....172

Starting from Scratch: Learning Roadmap If You Don’t Know Python....173

Pick Up a Book or Course That’s Easy to Understand....174

Start with Easy Questions on LeetCode, HackerRank, or Your Platform of Choice....174

Set a Measurable Target and Practice, Practice, Practice....175

Try Out ML-Related Python Packages....175

Coding Interview Success Tips....175

Think Out Loud....175

Control the Flow....176

Your Interviewer Can Help You Out....177

Optimize Your Environment....178

Interviews Require Energy!....178

Python Coding Interview: Data- and ML-Related Questions....179

Sample Data- and ML-Related Interview and Questions....179

FAQs for Data- and ML-Focused Interviews....187

Resources for Data and ML Interview Questions....188

Python Coding Interview: Brainteaser Questions....189

Patterns for Brainteaser Programming Questions....190

Resources for Brainteaser Programming Questions....198

SQL Coding Interview: Data-Related Questions....199

Resources for SQL Coding Interview Questions....201

Roadmaps for Preparing for Coding Interviews....201

Coding Interview Roadmap Example: Four Weeks, University Student....202

Coding Interview Roadmap Example: Six Months, Career Transition....204

Coding Interview Roadmap: Create Your Own!....205

Summary....205

Chapter 6. Technical Interview: Model Deployment and End-to-End ML....206

Model Deployment....207

The Main Experience Gap for New Entrants into the ML Industry....207

Should Data Scientists and MLEs Know This?....209

End-to-End Machine Learning....210

Cloud Environments and Local Environments....212

Overview of Model Deployment....215

Additional Tooling to Know....218

On-Device Machine Learning....219

Interviews for Roles Focused on Model Training....219

Model Monitoring....221

Monitoring Setups....221

ML-Related Monitoring Metrics....224

Overview of Cloud Providers....224

GCP....225

AWS....226

Microsoft Azure....227

Developer Best Practices for Interviews....227

Version Control....228

Dependency Management....229

Code Review....229

Tests....230

Additional Technical Interview Components....230

Machine Learning Systems Design Interview....231

Technical Deep-Dive Interview....234

Take-Home Exercise Tips....235

Product Sense....235

Sample Interview Questions on MLOps....236

Summary....238

Chapter 7. Behavioral Interviews....240

Behavioral Interview Questions and Responses....241

Use the STAR Method to Answer Behavioral Questions....242

Enhance Your Answers with the Hero’s Journey Method....243

Best Practices and Feedback from an Interviewer’s Perspective....246

Common Behavioral Questions and Recommendations....248

Questions About Communication Skills....248

Questions About Collaboration and Teamwork....249

Questions on How You Respond to Feedback....250

Questions on Dealing with Challenges and Learning New Skills....250

Questions About the Company....251

Questions About Work Projects....251

Free-Form Questions....252

Behavioral Interview Best Practices....252

How to Answer Behavioral Questions If You Don’t Have Relevant Work Experience....253

Senior+ Behavioral Interview Tips....254

Specific Preparation Examples for Big Tech....256

Amazon....256

Meta/Facebook....257

Alphabet/Google....258

Netflix....259

Summary....260

Chapter 8. Tying It All Together: Your Interview Roadmap....262

Interview Preparation Checklist....262

Interview Roadmap Template....263

Efficient Interview Preparation....265

Become a Better Learner....265

Time Management and Accountability....267

Avoid Burnout: It Is Costly....269

Impostor Syndrome....270

Summary....271

Chapter 9. Post-Interview and Follow-up....272

Post-Interview Steps....272

Take Notes of What You Remember from the Interview....273

Make Sure You’re Not Missing Important Information....273

Should You Send a Thank-You Email to the Interviewer?....273

Thank-You Note Template....273

How Long Should You Wait After the Interview for a Response Before Following Up?....275

What to Do Between Interviews....275

How to Respond to Rejections....275

Template for Rejection Responses....275

Job Applications Are a Funnel....276

Update and Customize Your Resume and Test Variations....277

Steps of the Offer Stage....278

Let Other Interviews-in-Progress Know You’ve Gotten an Offer....278

What to Do If the Offer Response Timeline Is Very Short....278

Understand Your Offer....279

First 30/60/90 Days of Your New ML Job....282

Gain Domain Knowledge....283

Gain Code Knowledge....283

Meet Relevant People....284

Help Improve the Onboarding Documentation....284

Keep Track of Your Achievements....284

Summary....285

Epilogue....286

Index....288

About the Author....308

Colophon....308

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