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From raw data pipelines to insight and production ML — the full data discipline.
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The data discipline spans a wider range of roles than almost any other in tech, from analysts building dashboards to data engineers running petabyte-scale pipelines to MLOps engineers deploying production models. These roles share a data-first mindset and often sit in the same org, but they recruit from different pools, require different technical depth, and carry very different compensation bands. Understanding where a role sits on the spectrum is the most important thing a recruiter can do before sourcing a single candidate.
Highlighted pills — primary tools most commonly listed in job descriptions for this discipline.
Data Scientist is one of the most variable titles in tech. Ask: "Is this role closer to analytics or to ML engineering?" A DS doing dashboard work and SQL reporting needs very different screening than one training production ML models. Clarify before sourcing.