An experienced machine-learning interviewer and interviewee takes readers through the highly selective recruitment process by sharing hard-won lessons she learned along the way.
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
Susan is a Principal Data Scientist at Elastic (of Elasticsearch), with previous ML experience in fintech at Canadian tech unicorn Clearco, social, and telecom. She is a Python Software Contributing member for her open-source contributions. Susan is also a frequent conference speaker, having given talks at 5 PyCons worldwide, as well as Data Science Europe, Toronto ML Summit, and Ubisoft. In her free time, she founded Quill Game Studios, which has scaled up to 10 staff.
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