NSXNational Skills ExchangeSign in
Back to Framework

Statisticians

SOC 15-2041.00Job Zone 5 · Extensive Preparationv.26.05

Context coveredThis 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.

Sources:O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.SkillsCrosswalkSkillsCrosswalk — O*NET enrichment + adjacency index. Opens in new tab.
Emerging
Entry / Apprentice
  1. Descriptive statistics and summary measures — compute and interpret under faculty or senior statistician guidance on assigned research datasets.
  2. Raw data files — organize, check for inaccuracies, and apply basic weighting procedures in preparation for processing on a research project team.
  3. Standard statistical software packages — execute pre-specified analyses and document outputs under direct supervision in an academic or applied research setting.
  4. Statistical tables, charts, and graphs — construct using spreadsheet or analytical software to present findings in structured internal reports.
  5. Sampling concepts and experimental designs — recognize and describe their appropriate application when reviewing existing study documentation.
  6. Research literature and statistical methods sections — read and summarize to support senior statisticians evaluating validity of published procedures.
  7. Relationships and trends in structured datasets — identify using guided exploratory analysis techniques within familiar data environments.
  8. Database query tools — retrieve and filter data from established repositories following documented protocols on a research or consulting team.
  9. Mathematical reasoning — apply foundational probability and inference concepts to verify calculations reviewed by a supervising statistician.
  10. Preliminary findings — present verbally to immediate project team members using prepared slide decks under direction from a project lead.
Developing
Mid-level / Established
  1. Statistical analysis plans — develop and execute routinely for moderately complex studies, adapting methods to meet user needs with limited oversight.
  2. Data quality and preprocessing pipelines — design and apply weighting, imputation, and adjustment procedures independently for standard research datasets.
  3. Validity and efficiency of statistical procedures — evaluate and document for ongoing projects, flagging methodological concerns to senior staff.
  4. Regression, ANOVA, and multivariate techniques — implement and interpret across familiar applied contexts including government, healthcare, or industry settings.
  5. Graphs, charts, and written reports — produce to communicate statistical results clearly to technical and semi-technical audiences in a professional environment.
  6. Sampling frame design and sample size determination — execute for survey or experimental studies using established methodological references.
  7. Statistical programming scripts — write and maintain in R, Python, or SAS to automate recurring analytical workflows within a departmental setting.
  8. Relationships and confounding factors in research data — identify and interpret, providing documented explanations of trends affecting study conclusions.
  9. Client or stakeholder meetings — present statistical findings using charts and bullets, responding to moderately complex questions with confidence.
  10. Business intelligence and data mining tools — apply to extract and synthesize patterns from large organizational datasets in support of ongoing projects.
Proficient
Senior / Expert IC
  1. Complex multivariable and longitudinal statistical models — design, validate, and interpret autonomously across diverse research domains including clinical trials, policy analysis, and industrial applications.
  2. Full-scope data preparation workflows — architect and execute for large-scale or non-standard datasets, resolving inaccuracies and structural anomalies without supervisory input.
  3. 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.
  4. Non-routine methodological challenges — diagnose and resolve, including violations of model assumptions, missing data patterns, and small-sample inference problems in real project environments.
  5. Comprehensive analytical reports — author for senior leadership, regulators, or peer-reviewed publication audiences, integrating statistical and contextual findings with precision and clarity.
  6. Experimental and quasi-experimental designs — develop and test end-to-end, including power analysis and adaptive design modifications, in research or operational settings.
  7. Advanced data mining and machine learning pipelines — build and critically evaluate, integrating statistical rigor with computational methods for high-dimensional datasets.
  8. Peer and client review sessions — lead independently, presenting nuanced statistical results and nonstatistical implications to mixed audiences including executives, scientists, and policymakers.
  9. Interdisciplinary research teams — serve as the statistical authority, advising collaborators on analytic strategy and interpreting quantitative evidence within broader scientific context.
  10. Systems of data collection and measurement — analyze for bias, efficiency, and fitness-for-purpose, recommending design improvements to organizational data infrastructure.
Advanced
Lead / Principal / Executive
  1. Organizational statistical strategy — define and champion methodological standards, governance frameworks, and quality benchmarks across an enterprise or major research institution.
  2. Novel statistical methodologies — pioneer and publish, advancing discipline knowledge and establishing best practices adopted by professional communities or regulatory bodies.
  3. Statistical workforce development — mentor, train, and evaluate teams of statisticians at multiple career levels, designing learning pathways aligned to organizational capability needs.
  4. Cross-functional analytical agendas — set in collaboration with C-suite or agency leadership, translating strategic priorities into rigorous quantitative research programs.
  5. Validity and integrity of large-scale data systems — oversee at the institutional level, establishing evaluation criteria and directing audit processes for enterprise analytical platforms.
  6. High-stakes statistical reports and expert testimony — author and present before regulatory agencies, legislative bodies, or executive boards, with full accountability for conclusions.
  7. Research design frameworks — establish for multi-site or longitudinal studies, coordinating statistical coherence across distributed teams and data sources.
  8. Organizational adoption of advanced analytical tools — lead, selecting and integrating business intelligence, data mining, and scientific software ecosystems to support strategic decision-making.
  9. 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.
  10. External partnerships and funding — cultivate with government agencies, industry sponsors, and academic consortia, positioning the organization as a recognized center of statistical excellence.

AI-at-Work Competency Framework

Sources:Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab.Jadhav & Danve, 2026Skill Automation Feasibility Index — Jadhav & Danve, 2026 (arXiv:2604.06906). Opens in new tab.WEF Skills TaxonomyWEF Skills Taxonomy 2021 — Building a Common Language for Skills at Work. Opens in new tab.

A 4-level framework describing how a worker at each mastery level uses, directs, and evaluates AI tools in this occupation. Each statement cites its evidence inline. Subscribe for $19.99/mo to read the full framework — or sign in if you have a subscription.

  1. Emerging
  2. Developing
  3. Proficient
  4. Advanced
Monthly subscription · Stripe-hosted · unlocks AI-at-Work and Pathsmith™ Durable Skills across the full library

Pathsmith™ Durable Skills Framework

Sources:Pathsmith™ Durable SkillsPathsmith™ Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Subscriber feature

Ten durable-skill domains mapped to four proficiency levels for this occupation. Subscribe to unlock the full Pathsmith™ Durable Skills Framework across all 1,016 occupations.

Monthly subscription · Stripe-hosted · unlocks AI-at-Work and Pathsmith™ Durable Skills across the full library
Show O*NET source anchors53 anchors · skillscrosswalk.com

O*NET enrichment · skillscrosswalk.com

Suggest an O*NET correction

Source anchors that ground each statement

Related titles
Analytical Statistician · Applied Scientist · Applied Statistician · Biometrician · Clinical Analyst · Data Analyst · Data Analyst Specialist · Data Analytics Specialist · Data Coordinator · Data Engineer · Data Manager · Data Modeler
RAPIDS apprenticeships
—
O*NET skills
MathematicsReading ComprehensionCritical ThinkingSpeakingActive ListeningComplex Problem SolvingWritingActive LearningScienceJudgment and Decision MakingLearning StrategiesMonitoringProgrammingSystems AnalysisSystems EvaluationTime Management
Knowledge domains
MathematicsComputers and ElectronicsEnglish Language
Abilities
Mathematical ReasoningNumber FacilityWritten ComprehensionOral ComprehensionInductive ReasoningOral ExpressionNear VisionWritten ExpressionDeductive ReasoningInformation Ordering
Work styles
Attention to DetailIntellectual CuriosityDependabilityAchievement OrientationIntegrityCautiousness
Technology
Data base user interface and query softwareData mining softwareData base management system softwareBusiness intelligence and data analysis softwareAnalytical or scientific softwareObject or component oriented development softwareDevelopment environment softwareEnterprise application integration softwareOperating system softwareSpreadsheet software
Tasks · seed anchors for statements
  1. Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
  2. Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
  3. Report results of statistical analyses, including information in the form of graphs, charts, and tables.
  4. Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
  5. Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
  6. Develop and test experimental designs, sampling techniques, and analytical methods.
  7. Identify relationships and trends in data, as well as any factors that could affect the results of research.
  8. Present statistical and nonstatistical results, using charts, bullets, and graphs, in meetings or conferences to audiences such as clients, peers, and students.
CIP education codes
13.060313.060413.060813.069926.110126.110226.131127.010127.030127.030427.050127.050227.050327.059927.060130.490130.700130.709930.710130.710230.719942.270845.010245.010345.060352.1302

Sources: O*NET v30.2 (CC BY 4.0), SkillsCrosswalk.com, LER.me®, Anthropic Economic Index, SAFI (Jadhav & Danve, 2026), WEF Skills Taxonomy 2021, Pathsmith™ Durable Skills Framework. © 2026 EBSCOed.