Where data science meets real engineering, you build the systems that take models from a notebook to production — pipelines, deployment, and the infrastructure that makes data science actually run. Turning experiments into working systems.
Part data science, part software engineering, you build pipelines, deploy models, and engineer the infrastructure around them — working with scientists and engineers, mostly at a screen. Making a model reliable in production is the craft, since a notebook model often breaks in production, in ways no one predicted.
The harder part is straddling two fast-moving fields — you need data science and engineering both, and the tooling churns constantly. Data is messy, requirements shift, and a lot of the work is making fragile things robust. Scope varies by company, from pure pipelines to full ML platforms.
It tends to fit someone technically broad, pragmatic, and comfortable with ambiguity. If you want pure modeling or pure software, the hybrid may not suit. But if making data science actually work at scale appeals, the work tends to be steadily engaging, system by system.
Where this role sits in the broader career landscape — and where it can take you.
Roles like this one sit within a broader occupational category. The numbers below reflect that full landscape — helpful for context, but your specific experience will depend on level, specialty, and where you work.
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