Technology
A data scientist builds statistical and machine learning models that predict outcomes, find patterns and guide decisions. The role matters because well-built models can improve forecasting, targeting and risk decisions, but only when tied to a real business problem.
Median pay
$120,230a year
Data scientists, U.S. BLS, May 2025
The role
Companies hire one when they have reliable data and a clear prediction or optimization problem, such as churn, demand forecasting or fraud detection.
Typical responsibilities
Core skills
Tools and systems
Related titles
Typical background
Typically a master's degree in statistics, computer science or a quantitative field, plus 3 to 6 years of applied modeling experience.
At a glance
The essentials for planning the hire: level, background, how you can hire and what the role typically pays.
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FAQ
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A data scientist builds statistical and machine learning models that predict outcomes, find patterns and guide decisions. The role matters because well-built models can improve forecasting, targeting and risk decisions, but only when tied to a real business problem. Typical responsibilities include: frame business problems as questions a model can answer; build, train and validate predictive and statistical models; design experiments and A/B tests and interpret results.
Look for machine learning, statistics, python, feature engineering, experiment design and model validation. Experience with Python, scikit-learn, Jupyter, SQL and TensorFlow is common in this role.
Yes. Employers hire a data scientist on contract for projects, peak periods and cover, and contract-to-hire is a good way to confirm fit before a permanent offer. For long-term needs, most choose full-time placement.
Not always. A data scientist needs clean, accessible data tied to a clear business question. If reporting is still unreliable, hire a data engineer or analyst first. Models built on weak data waste time and produce results people rightly distrust.
A data scientist explores data, builds and tests models and explains results. A machine learning engineer puts models into production and keeps them running. Hire a scientist to find out what works, and an ML engineer when a proven model must be deployed at scale.
Related roles
Every role in the practice is recruited by the same specialist team.
