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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.
Machine Learning (ML) is an integral part of our day to day, whether we’re aware of it or not. Each time you go on sites like YouTube and Amazon.com, you’re interacting with ML, which powers personalized recommendations. This means that the way the products are displayed on the sites is based on what ML algorithms think suit your taste and interests. And not just that—there’s ML-based comment moderation to flag spam or toxic comments, review moderation, and more. On sites like YouTube, there are ML-generated captions and translations.
ML is also present in aspects of our lives beyond shopping and entertainment. For example, when you send a money transfer online, ML algorithms are checking to see whether it’s fraudulent. We live in an age of software that is built on a foundation of data and ML algorithms.
All of this software requires specialized talent to design and build, which has created a demand for software skills and has elevated ML careers in recent years. The pay for technology roles has also risen as a result. These are just some of the many factors that make an ML career enticing: building the products and product features that are so integral to our lives. Since ML techniques power AI advancements, this discussion similarly applies to “AI careers.”
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
Who This Book Is For:
Before I dive into the chapters, I want to outline the following 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