Applied / Research Scientist, ML Engineer: What’s the Difference?
Eugene Yan
The article explains how the data scientist role has split into distinct positions—data scientist, applied scientist, research scientist, and machine learning engineer—each with different goals, skills, and deliverables. Data scientists perform analysis to guide decisions using SQL and Python, applied scientists build ML systems for business outcomes using ML libraries and DevOps tools, research scientists develop new methodology on academic benchmarks, and ML engineers build infrastructure platforms using languages like Java and Go. This specialization helps clarify job expectations and lets practitioners focus on their strengths, though it risks fragmentation, responsibility diffusion, and title inflation that could harm hiring competitiveness.
Why it matters
A personal take on their deliverables and skills, and what it means for the industry and your team.
