As someone in the trenches of data science hiring for over 7 years, I've watched our field transform dramatically. Recently, a job description for an ML role caught my eye - and not necessarily in a good way. It got me thinking about how our industry's hiring practices often need to catch up to the reality of our work. Let me share some observations:
The Commodity of Code
- LLM can generate working solutions / provide ideas / get started on any topic as long as you have good basic skills and coding knowledge. Now, I ask interns hiring assignment tasks to focus on accuracy and bugs. Code has become a commodity. The real value lies in understanding models, and limitations, bridging the gap between visions and technical realities, and architecting solutions that solve real-world problems.
The Kitchen Sink JD
- This particular job description reads like a wish list for a tech superhero. Data structures, algorithms, AI/ML, coding, system design - oh, and don't forget a dash of product sense! While it's great to aim high, this scattergun approach often misses the mark. We need specialists with deep expertise, not generalists who've dabbled in everything.
The Interview Gauntlet
- The hiring process outlined was a marathon: write-ups, HackerEarth assessments, coding tests, multiple rounds with the ML team, and then more conversations. In a market where top talent is scarce and in high demand, do we really need to put candidates through such a lengthy ordeal?
The Missing Pieces
- What struck me most was what the JD and process didn't emphasize. Where was the assessment of a candidate's ability to translate business problems into technical solutions? How about evaluating their capacity to stay ahead of rapidly evolving trends in ML?
A Call for Pragmatism
- To my fellow hiring managers and HR teams: let's get practical. The perfect candidate who ticks every box on your mile-long list probably doesn't exist - and if they do, they're likely happily employed or running their own startup.
Instead, focus on core competencies that drive real value:
- The ability to understand and translate business needs
- A knack for architecting scalable, efficient solutions
- Adaptability and a passion for continuous learning
- Strong communication skills to bridge technical and non-technical stakeholders
The ML landscape is changing faster than ever. Our hiring practices need to keep pace. Let's move beyond the "code on a whiteboard" era and design processes that identify true innovators who can propel our field forward.
Another Good Read - Why We Don't Interview Product Managers Anymore
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- Focus on practical software engineering, not algorithm challenges.
- Work through a system design problem relevant to your daily work.
- Talk about your perspectives on building a great product.
- Deep dive on engineering practices and culture
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