Statistical Assistants

SOC 43-9111.00Job Zone 4 · Considerable Preparationv.26.05

Context coveredThis framework covers statistical support and analytical work performed in office, research, government, and administrative environments where practitioners compute, verify, manage, and communicate data-driven findings across the full range of Job Zone 4 preparation and experience.

Sources:O*NET v30.2 SkillsCrosswalk

Emerging

Entry / Apprentice

Statistical formulas and calculators — apply under direct supervision to compute basic descriptive statistics for assigned datasets in an office or research support environment.
Source data completeness and accuracy — verify by cross-referencing original survey forms or records against entered values under guidance from a senior analyst.
Data entry tasks — execute with attention to detail by inputting coded records into statistical software or database systems following established protocols.
Data coding lists — interpret and apply to categorize raw survey responses prior to computer entry in a structured administrative workflow.
Standard office suite software — use to organize and format tabular data outputs in support of team reporting tasks.
Database user interface tools — navigate under supervision to retrieve and file data records according to departmental data management procedures.
Survey forms and printed reports — sort, label, and organize for distribution or analysis following defined paperwork-handling procedures.
Basic statistical charts and graphs — produce using analytical software templates to illustrate preliminary findings for supervisor review.
Written comprehension skills — demonstrate by reading and following detailed procedural manuals and codebooks relevant to assigned statistical support tasks.
Time management principles — apply by prioritizing routine data entry and verification tasks to meet established project deadlines in a team-based setting.

Developing

Mid-level / Established

Statistical computations and trend analyses — perform routinely using analytical software such as SAS or SPSS to support research or operational reporting with reduced oversight.
Source data audits — conduct independently by designing and executing completeness and accuracy checks across multiple data files in a production database environment.
Data compilation reports — prepare by integrating results from multiple analyses into coherent charts, graphs, and narrative summaries for internal or external stakeholders.
Database query tools — use proficiently to extract, update, and maintain large datasets in support of ongoing statistical projects.
Data coding schemes — develop and refine for recurring survey instruments, ensuring consistent classification across data collection cycles.
Publication-ready data tables — assemble by formatting and validating statistical outputs for inclusion in departmental or government reports.
Complex problem-solving techniques — apply when reconciling discrepancies between source records and database entries across high-volume data sets.
Graphics and photo imaging software — utilize to enhance the visual clarity of analytical charts and figures prepared for presentation or publication.
Critical thinking skills — employ to evaluate the appropriateness of selected statistical methods relative to project data types and research objectives.
Document management software — use to organize, version-control, and archive project files and analytical records within established information governance frameworks.

Proficient

Senior / Expert IC

Advanced statistical analyses — design and execute autonomously using programming languages such as Python or R to address non-routine analytical questions across the full project lifecycle.
Multi-source data integration — lead by merging and reconciling datasets from disparate systems to ensure analytical integrity for complex research or policy projects.
Comprehensive analytical reports — author independently, translating statistical findings into clear, evidence-based narratives suitable for diverse professional audiences.
Data quality assurance frameworks — implement by establishing validation rules and audit workflows that systematically detect and resolve data errors organization-wide.
Database architecture and query optimization — apply using SQL or equivalent tools to maintain high-performance data environments supporting concurrent analytical workloads.
Statistical publication processes — manage end-to-end by coordinating data preparation, peer review, and formatting for official release in accordance with agency standards.
Judgment and decision-making competency — exercise when selecting appropriate modeling approaches, interpreting ambiguous results, and recommending corrective analytical actions.
Active learning strategies — employ by independently evaluating emerging statistical methodologies and integrating relevant techniques into current project work.
CRM and financial analysis software — leverage to align statistical outputs with operational or business intelligence objectives across cross-functional teams.
Inductive and deductive reasoning — apply systematically to identify patterns in complex datasets and draw valid, defensible conclusions for organizational decision support.

Advanced

Lead / Principal / Executive

Organizational statistical strategy — set direction for by defining standards, methodologies, and technology roadmaps that govern data analysis practices across the enterprise.
Competency development programs — design and deliver for statistical assistant teams, mentoring staff in advanced analytical techniques, software proficiency, and quality assurance.
Cross-departmental data governance — lead by establishing policies for data integrity, security, and lifecycle management that align with regulatory and organizational requirements.
Enterprise-scale publication initiatives — oversee by directing the end-to-end production of statistical reports, datasets, and public-facing data releases for large agencies or organizations.
Senior stakeholder communication — drive by translating complex statistical insights into strategic recommendations presented to executive leadership and external partners.
Institutional analytical frameworks — architect by integrating development environment software, object-oriented programming tools, and database platforms into unified, scalable data pipelines.
Quality and performance standards — establish for statistical support functions, defining measurable benchmarks and audit mechanisms to ensure consistent accuracy and reliability.
Research partnerships and contracts — negotiate and manage by representing the organization's statistical capabilities with academic institutions, government bodies, or industry clients.
Workforce planning for statistical operations — lead by assessing team capacity, identifying skill gaps, and directing recruitment and training strategies to meet evolving analytical demands.
Innovation in statistical methodology — champion at the organizational level by evaluating advanced modeling techniques, piloting new analytical tools, and institutionalizing best practices across the field.

AI-at-Work Competency Framework

Sources:Anthropic Economic Index Jadhav & Danve, 2026 WEF Skills Taxonomy

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Developing
Proficient
Advanced

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Sources:Pathsmith™ Durable Skills O*NET v30.2

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Related titles

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RAPIDS apprenticeships

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O*NET skills

MathematicsCritical ThinkingReading ComprehensionComplex Problem SolvingActive LearningWritingActive ListeningSpeakingTime ManagementJudgment and Decision MakingMonitoringProgramming

Knowledge domains

English LanguageMathematicsComputers and ElectronicsCustomer and Personal ServiceEducation and TrainingAdministrative

Abilities

Mathematical ReasoningWritten ComprehensionNumber FacilityOral ComprehensionWritten ExpressionOral ExpressionInformation OrderingNear VisionInductive ReasoningDeductive Reasoning

Work styles

Attention to DetailDependabilityCautiousnessIntegrityAchievement OrientationIntellectual Curiosity

Technology

Development environment softwareCustomer relationship management CRM softwareAnalytical or scientific softwareComputer aided design CAD softwareObject or component oriented development softwareOffice suite softwareData base user interface and query softwareFinancial analysis softwareGraphics or photo imaging softwareDocument management software

Tasks · seed anchors for statements

Compute and analyze data, using statistical formulas and computers or calculators.
Check source data to verify completeness and accuracy.
Enter data into computers for use in analyses or reports.
Compile reports, charts, or graphs that describe and interpret findings of analyses.
Participate in the publication of data or information.
File data and related information, and maintain and update databases.
Organize paperwork, such as survey forms or reports, for distribution or analysis.
Code data prior to computer entry, using lists of codes.

CIP education codes

52.0302