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
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. Susan is an international speaker, with 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 on her Substack newsletter. In her free time she leads a team of game developers under Quill Game Studios, with multiple games released on consoles and Steam.
