Description
Design, code, and validate statistical programs to ingest, transform, analyze, and visualize data, translating statistical plans into reproducible, audit-ready outputs. Build analysis datasets, automate reporting, and collaborate with statisticians to deliver accurate, timely insights across domains.
- • Translate statistical analysis plans into efficient, reproducible code (e.g., R, SAS, Python).
- • Build, clean, and transform raw data into analysis-ready datasets with robust quality checks.
- • Program statistical models and tests; compute estimates, confidence intervals, and p-values.
- • Generate and automate tables, listings, figures, and dashboards.
- • Develop reusable functions, macros, and packages; enforce coding standards.
- • Validate programs via independent programming, unit tests, and peer review.
- • Optimize performance and memory for large-scale data processing.
- • Implement exploratory data analysis pipelines to summarize trends and anomalies.
- • Design and maintain automated workflows using version control and CI/CD.
- • Document specifications, data lineage, and code to ensure traceability and auditability.
- • Collaborate with statisticians to ensure correct implementation of methods and assumptions.
- • Program sampling, randomization, and resampling procedures as specified.
- • Integrate data from databases, APIs, and files; build ETL processes and manage metadata.
- • Monitor data quality; implement validation rules and discrepancy reports.
- • Ensure compliance with organizational, regulatory, and privacy standards.
- • Present programmed methods and results to technical and nontechnical stakeholders.
- • Maintain secure, reproducible computing environments and manage dependencies.
- • Support deployment of analyses to notebooks, reports, and containers.
- • Manage code reviews, branching, and releases across development and production environments.
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O*NET occupational data (work activities, skills, knowledge).
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Last reviewed: Jan 2026
