Description
Develop and apply mathematical statistics to create, analyze, and validate statistical models and methods. Derive theoretical properties, design inference and sampling strategies, implement algorithms, and communicate results to support research, policy, and operational decisions.
- • Develop new estimators, tests, and models, and derive properties such as bias, variance, and consistency.
- • Formulate and prove results in probability and inference to support new or improved methods.
- • Design sampling schemes and experimental designs; perform sample size and power calculations.
- • Construct and validate likelihood, Bayesian, and nonparametric approaches for targeted problems.
- • Evaluate identifiability, assumptions, and robustness to model misspecification.
- • Develop and optimize algorithms for estimation, inference, and simulation.
- • Implement methods in statistical software (e.g., R, Python, C++) with reproducible, validated code.
- • Run simulation and resampling studies to assess accuracy, efficiency, and coverage.
- • Create and document weighting, imputation, and missing-data procedures.
- • Develop variance estimation and uncertainty quantification, including confidence and prediction intervals.
- • Assess and improve survey designs, calibration, and small-area estimation techniques.
- • Plan rigorous data collection protocols and define sampling frames and inclusion criteria.
- • Evaluate data quality, measurement error, and bias; recommend corrections or adjustments.
- • Select appropriate statistical approaches aligned with research, regulatory, or policy needs.
- • Prepare technical reports, peer-reviewed papers, and methodological guidance with proofs and diagnostics.
- • Present complex statistical results to technical and nontechnical audiences using clear graphics and summaries.
- • Review and validate collaborators’ analyses for methodological soundness and reproducibility.
- • Develop standards, test plans, and quality assurance procedures for statistical methods and software.
- • Mentor analysts and statisticians in statistical theory, methods, and proper application.
Related specializations
Interview options
Interview options
Interviewee gender
Interviewee accent
Interview time
Related Pathways
Public Service & Safety
View
Source
Tasks & skills:
O*NET occupational data (work activities, skills, knowledge).
Learn more
Sources & Standards:
This site includes information from O*NET by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under the CC BY 4.0 license. Career Clutch has modified some of this information for student readability. USDOL/ETA has not approved, endorsed, or tested these modifications. O*NET® is a trademark of USDOL/ETA.
Last reviewed: Jan 2026
