Statisticians and data scientists design experiments, build predictive models, and analyze complex datasets to answer questions and solve problems for organizations. They apply mathematical and computational techniques—ranging from classical hypothesis testing to machine learning—to extract patterns from data in fields as diverse as healthcare, finance, marketing, sports, and government policy. Strong coding skills (Python, R, SQL) combined with statistical theory and domain knowledge make this a highly interdisciplinary role.
| Entry level | $70,000 |
| Median | $108,000 |
| Senior | $150,000 |
| Top 10% | $190,000 |
| Job growth | +35% |
| Professionals in the USA | 1.0 million |
| Typical hours/week | 42 hrs |
| Remote work share | 55% |
| Annual job openings | 45,000/yr |
| Demand | Very High |
AI and automated machine learning tools are rapidly handling routine data cleaning, exploratory analysis, and basic model building, shifting the role toward interpretation, strategy, and communication. Data scientists who focus purely on standard modeling tasks face growing competition from automated pipelines, while those who bridge business context and advanced methodology remain highly valuable.
Automation exposure: Data cleaning, feature engineering, basic statistical testing, standard regression/classification model building, dashboard creation, and routine reporting are increasingly automated by AutoML platforms and AI coding assistants.
The human edge: Framing ambiguous business problems, designing valid experiments, judging causal inference and model assumptions, communicating nuanced uncertainty to stakeholders, and ensuring ethical, unbiased use of data remain distinctly human strengths.
Figures are estimates for exploration — verify current data with BLS.gov.