{"schemaVersion":"1.0","exportedAt":"2026-05-15T12:40:46.547Z","occupation":{"soc":"15-2041.00","title":"Statisticians","group":"Computer & Mathematical","sector":"54","jobZone":5,"jobZoneInferred":false},"framework":{"version":"v.26.05","description":"","contextCovered":"This framework covers statisticians working across applied research, government, healthcare, policy, and industry settings who design studies, analyze complex data, and communicate quantitative findings to diverse stakeholders.","levels":{"emerging":{"label":"Emerging","statements":["Descriptive statistics and summary measures — compute and interpret under faculty or senior statistician guidance on assigned research datasets.","Raw data files — organize, check for inaccuracies, and apply basic weighting procedures in preparation for processing on a research project team.","Standard statistical software packages — execute pre-specified analyses and document outputs under direct supervision in an academic or applied research setting.","Statistical tables, charts, and graphs — construct using spreadsheet or analytical software to present findings in structured internal reports.","Sampling concepts and experimental designs — recognize and describe their appropriate application when reviewing existing study documentation.","Research literature and statistical methods sections — read and summarize to support senior statisticians evaluating validity of published procedures.","Relationships and trends in structured datasets — identify using guided exploratory analysis techniques within familiar data environments.","Database query tools — retrieve and filter data from established repositories following documented protocols on a research or consulting team.","Mathematical reasoning — apply foundational probability and inference concepts to verify calculations reviewed by a supervising statistician.","Preliminary findings — present verbally to immediate project team members using prepared slide decks under direction from a project lead."]},"developing":{"label":"Developing","statements":["Statistical analysis plans — develop and execute routinely for moderately complex studies, adapting methods to meet user needs with limited oversight.","Data quality and preprocessing pipelines — design and apply weighting, imputation, and adjustment procedures independently for standard research datasets.","Validity and efficiency of statistical procedures — evaluate and document for ongoing projects, flagging methodological concerns to senior staff.","Regression, ANOVA, and multivariate techniques — implement and interpret across familiar applied contexts including government, healthcare, or industry settings.","Graphs, charts, and written reports — produce to communicate statistical results clearly to technical and semi-technical audiences in a professional environment.","Sampling frame design and sample size determination — execute for survey or experimental studies using established methodological references.","Statistical programming scripts — write and maintain in R, Python, or SAS to automate recurring analytical workflows within a departmental setting.","Relationships and confounding factors in research data — identify and interpret, providing documented explanations of trends affecting study conclusions.","Client or stakeholder meetings — present statistical findings using charts and bullets, responding to moderately complex questions with confidence.","Business intelligence and data mining tools — apply to extract and synthesize patterns from large organizational datasets in support of ongoing projects."]},"proficient":{"label":"Proficient","statements":["Complex multivariable and longitudinal statistical models — design, validate, and interpret autonomously across diverse research domains including clinical trials, policy analysis, and industrial applications.","Full-scope data preparation workflows — architect and execute for large-scale or non-standard datasets, resolving inaccuracies and structural anomalies without supervisory input.","Statistical methodology selection — evaluate and justify the most appropriate techniques for novel user needs or research questions, drawing on breadth of theoretical and applied knowledge.","Non-routine methodological challenges — diagnose and resolve, including violations of model assumptions, missing data patterns, and small-sample inference problems in real project environments.","Comprehensive analytical reports — author for senior leadership, regulators, or peer-reviewed publication audiences, integrating statistical and contextual findings with precision and clarity.","Experimental and quasi-experimental designs — develop and test end-to-end, including power analysis and adaptive design modifications, in research or operational settings.","Advanced data mining and machine learning pipelines — build and critically evaluate, integrating statistical rigor with computational methods for high-dimensional datasets.","Peer and client review sessions — lead independently, presenting nuanced statistical results and nonstatistical implications to mixed audiences including executives, scientists, and policymakers.","Interdisciplinary research teams — serve as the statistical authority, advising collaborators on analytic strategy and interpreting quantitative evidence within broader scientific context.","Systems of data collection and measurement — analyze for bias, efficiency, and fitness-for-purpose, recommending design improvements to organizational data infrastructure."]},"advanced":{"label":"Advanced","statements":["Organizational statistical strategy — define and champion methodological standards, governance frameworks, and quality benchmarks across an enterprise or major research institution.","Novel statistical methodologies — pioneer and publish, advancing discipline knowledge and establishing best practices adopted by professional communities or regulatory bodies.","Statistical workforce development — mentor, train, and evaluate teams of statisticians at multiple career levels, designing learning pathways aligned to organizational capability needs.","Cross-functional analytical agendas — set in collaboration with C-suite or agency leadership, translating strategic priorities into rigorous quantitative research programs.","Validity and integrity of large-scale data systems — oversee at the institutional level, establishing evaluation criteria and directing audit processes for enterprise analytical platforms.","High-stakes statistical reports and expert testimony — author and present before regulatory agencies, legislative bodies, or executive boards, with full accountability for conclusions.","Research design frameworks — establish for multi-site or longitudinal studies, coordinating statistical coherence across distributed teams and data sources.","Organizational adoption of advanced analytical tools — lead, selecting and integrating business intelligence, data mining, and scientific software ecosystems to support strategic decision-making.","Ethical and policy dimensions of statistical practice — guide at the institutional level, ensuring data privacy, equity in measurement, and responsible use of inference across all projects.","External partnerships and funding — cultivate with government agencies, industry sponsors, and academic consortia, positioning the organization as a recognized center of statistical excellence."]}}},"sources":{"onet":"v30.2 (CC BY 4.0)","crosswalk":"https://skillscrosswalk.com","generator":"LER.me"},"attribution":"© EBSCOed"}