Frontmatter Roy Keyes, Author of Hiring Data Scientists and Machine Learning Engineers: A Practical Guide
Aug 11, 2021
Roy Keyes, a data scientist and consulting expert, shares his journey from studying physics in Kansas to building successful teams in tech startups. He discusses the challenges of hiring in data science, stressing the importance of clear goals and treating candidates like customers. Roy also tackles the impact of ML in finding product-market fit and explains the nuances of data science versus AI. He talks about his decision to write a practical guide on hiring practices while navigating the challenges of self-publishing during the pandemic.
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Transcript
What Data Science, ML, And AI Mean Today
- 'Data science' spans analytics, ML, and production work like ETL and dashboards.
- Today ML dominates the AI conversation, but 'AI' is often marketing while 'ML' is the technical reality.
Define Roles From Business Goals
- Start hiring by specifying exactly what you need and why, then map roles to those goals.
- Crisp role descriptions reduce wasted applications and mismatched expectations.
Huge Volume At Junior Levels, Scarcity Seniorly
- Hiring data scientists brings massive candidate volume at junior levels but scarcity at senior levels.
- You must design processes that scale for large applicant pools while handling senior searches differently.
