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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 v30.2O*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. Statistical formulas and calculators — apply under direct supervision to compute basic descriptive statistics for assigned datasets in an office or research support environment.
  2. Source data completeness and accuracy — verify by cross-referencing original survey forms or records against entered values under guidance from a senior analyst.
  3. Data entry tasks — execute with attention to detail by inputting coded records into statistical software or database systems following established protocols.
  4. Data coding lists — interpret and apply to categorize raw survey responses prior to computer entry in a structured administrative workflow.
  5. Standard office suite software — use to organize and format tabular data outputs in support of team reporting tasks.
  6. Database user interface tools — navigate under supervision to retrieve and file data records according to departmental data management procedures.
  7. Survey forms and printed reports — sort, label, and organize for distribution or analysis following defined paperwork-handling procedures.
  8. Basic statistical charts and graphs — produce using analytical software templates to illustrate preliminary findings for supervisor review.
  9. Written comprehension skills — demonstrate by reading and following detailed procedural manuals and codebooks relevant to assigned statistical support tasks.
  10. 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
  1. Statistical computations and trend analyses — perform routinely using analytical software such as SAS or SPSS to support research or operational reporting with reduced oversight.
  2. Source data audits — conduct independently by designing and executing completeness and accuracy checks across multiple data files in a production database environment.
  3. Data compilation reports — prepare by integrating results from multiple analyses into coherent charts, graphs, and narrative summaries for internal or external stakeholders.
  4. Database query tools — use proficiently to extract, update, and maintain large datasets in support of ongoing statistical projects.
  5. Data coding schemes — develop and refine for recurring survey instruments, ensuring consistent classification across data collection cycles.
  6. Publication-ready data tables — assemble by formatting and validating statistical outputs for inclusion in departmental or government reports.
  7. Complex problem-solving techniques — apply when reconciling discrepancies between source records and database entries across high-volume data sets.
  8. Graphics and photo imaging software — utilize to enhance the visual clarity of analytical charts and figures prepared for presentation or publication.
  9. Critical thinking skills — employ to evaluate the appropriateness of selected statistical methods relative to project data types and research objectives.
  10. Document management software — use to organize, version-control, and archive project files and analytical records within established information governance frameworks.
Proficient
Senior / Expert IC
  1. 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.
  2. Multi-source data integration — lead by merging and reconciling datasets from disparate systems to ensure analytical integrity for complex research or policy projects.
  3. Comprehensive analytical reports — author independently, translating statistical findings into clear, evidence-based narratives suitable for diverse professional audiences.
  4. Data quality assurance frameworks — implement by establishing validation rules and audit workflows that systematically detect and resolve data errors organization-wide.
  5. Database architecture and query optimization — apply using SQL or equivalent tools to maintain high-performance data environments supporting concurrent analytical workloads.
  6. Statistical publication processes — manage end-to-end by coordinating data preparation, peer review, and formatting for official release in accordance with agency standards.
  7. Judgment and decision-making competency — exercise when selecting appropriate modeling approaches, interpreting ambiguous results, and recommending corrective analytical actions.
  8. Active learning strategies — employ by independently evaluating emerging statistical methodologies and integrating relevant techniques into current project work.
  9. CRM and financial analysis software — leverage to align statistical outputs with operational or business intelligence objectives across cross-functional teams.
  10. 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
  1. Organizational statistical strategy — set direction for by defining standards, methodologies, and technology roadmaps that govern data analysis practices across the enterprise.
  2. Competency development programs — design and deliver for statistical assistant teams, mentoring staff in advanced analytical techniques, software proficiency, and quality assurance.
  3. Cross-departmental data governance — lead by establishing policies for data integrity, security, and lifecycle management that align with regulatory and organizational requirements.
  4. 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.
  5. Senior stakeholder communication — drive by translating complex statistical insights into strategic recommendations presented to executive leadership and external partners.
  6. Institutional analytical frameworks — architect by integrating development environment software, object-oriented programming tools, and database platforms into unified, scalable data pipelines.
  7. Quality and performance standards — establish for statistical support functions, defining measurable benchmarks and audit mechanisms to ensure consistent accuracy and reliability.
  8. Research partnerships and contracts — negotiate and manage by representing the organization's statistical capabilities with academic institutions, government bodies, or industry clients.
  9. Workforce planning for statistical operations — lead by assessing team capacity, identifying skill gaps, and directing recruitment and training strategies to meet evolving analytical demands.
  10. 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 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.
Subscriber feature

Authoritative source data identified for 998 occupations

How a worker at each mastery level uses, directs, and evaluates AI tools in this occupation. Each statement cites its evidence inline; click a citation chip to verify the source.

Emerging
  1. AI-generated summary statistics — accepts descriptive outputs (means, standard deviations, frequency counts) produced by an LLM or AI-enabled spreadsheet tool, cross-checking results against source data to confirm basic accuracy Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab..
  2. Data entry assistance — uses AI autocomplete or form-filling tools to accelerate routine data input tasks, manually reviewing each record for completeness before submission Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab..
Developing
  1. Statistical formula selection — consults an AI assistant to identify appropriate formulas or test types for a given dataset, then applies the chosen method independently and verifies outputs against known benchmarks Jadhav & Danve, 2026Skill Automation Feasibility Index — Jadhav & Danve, 2026 (arXiv:2604.06906). Opens in new tab..
  2. Source data validation — delegates initial completeness and range checks to an AI tool, then personally reviews flagged anomalies and confirms corrections against original source documents 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..
  3. Report drafting support — directs an AI assistant to produce a first-draft narrative interpretation of statistical findings, editing the text for accuracy, appropriate caveats, and alignment with the intended audience Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab..
Proficient
  1. Analytical workflow orchestration — structures a multi-step analysis by assigning data-cleaning, computation, and preliminary charting tasks to AI tools, while retaining ownership of hypothesis framing and interpretive conclusions Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab. WEF Skills TaxonomyWEF Skills Taxonomy 2021 — Building a Common Language for Skills at Work. Opens in new tab..
  2. Chart and graph quality control — evaluates AI-generated visualizations against the underlying data, correcting axis scaling, labeling, and visual encoding choices before incorporating outputs into official reports Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab..
  3. Publication-ready output pipeline — coordinates AI assistance across data entry, computation, and narrative compilation to reduce end-to-end turnaround, achieving substantial time savings while personally signing off on every substantive finding Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab..
  4. Critical error detection — applies mathematical reasoning to identify implausible AI-generated statistics that fall outside expected distributions, escalating discrepancies for human review rather than accepting outputs at face value 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..
Advanced
  1. Displacement-risk mitigation strategy — redesigns the statistical assistant's workflow to concentrate human effort on judgment-intensive tasks (contextual interpretation, stakeholder communication, anomaly investigation) that resist automation, given the occupation's high displacement risk profile 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..
  2. AI autonomy calibration — sets task-specific autonomy boundaries for AI tools across the full analysis lifecycle, granting higher autonomy on well-defined computational subtasks while enforcing human sign-off on findings that inform policy or publication decisions 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..
  3. Cross-functional AI integration — leads the adoption and evaluation of AI-enabled statistical tooling within an administrative unit, training colleagues on appropriate collaboration patterns and establishing quality-control checkpoints that preserve data integrity WEF Skills TaxonomyWEF Skills Taxonomy 2021 — Building a Common Language for Skills at Work. Opens in new tab..
  4. Complex problem decomposition — breaks multivariable analytical challenges into discrete subtasks, delegates computationally tractable components to AI agents, synthesizes partial outputs into a coherent conclusion, and documents the reasoning chain for audit purposes Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab. WEF Skills TaxonomyWEF Skills Taxonomy 2021 — Building a Common Language for Skills at Work. Opens in new tab..
Evidence pack
AEI usage
Task observations: 5575
Augment share: 95%
Time saved: 89.7%
AI autonomy: 2.69
SAFI positioning
Top skill: Mathematics
Score: 73.2 / 100
Quadrant: Q1_high_displacement_risk
precision: exact
WEF cluster
Human-Technology Interaction
human_technology_interaction
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.

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/role levels for each occupation. Each statement is aligned to the Pathsmith taxonomy, derived from trusted grounding data and mapped to occupation-specific O*NET tasks and skills.

1Communication12 statements
Emerging
  1. Data findings summary — drafts basic written descriptions of statistical outputs using standard report templates in supervised contexts 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.
  2. Stakeholder terminology — uses foundational statistical vocabulary when verbally relaying data results to supervisors or team members Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Survey form explanation — communicates the purpose and completion instructions of survey instruments to respondents in straightforward terms O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Developing
  1. Statistical report narrative — composes clear written sections describing findings from data analyses with limited editorial review for internal audiences 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.
  2. Data discrepancy communication — articulates data completeness or accuracy issues to supervisors through concise verbal or written summaries 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.
  3. Chart and graph annotation — labels and captions visual data displays so that non-technical audiences can interpret findings independently O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Applying
  1. Cross-functional findings briefing — presents compiled statistical reports, charts, and graphs to mixed technical and non-technical audiences with clarity and precision 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.
  2. Publication-ready writing — produces polished written narratives for data publications that accurately characterize analytical findings without ambiguity O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
  3. Active listening in data review — elicits clarification from analysts or clients about data requirements through targeted questioning during project intake meetings Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Exceeding
  1. Audience-adaptive data storytelling — translates complex statistical findings into tailored communications for executives, researchers, and public stakeholders simultaneously 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.
  2. Documentation standards authorship — develops and disseminates report writing guidelines and communication templates adopted across the statistical support team Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Cross-agency data communication — leads coordination calls with external partners to align on data definitions, coding schemes, and reporting formats O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
2Leadership10 statements
Emerging
  1. Task ownership initiation — takes responsibility for assigned data entry or coding tasks through to completion without requiring repeated direction Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Peer support offer — volunteers to assist colleagues with database file organization or report formatting during periods of lower individual workload Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Developing
  1. Workflow self-direction — organizes own daily priorities across multiple data projects, data filing, and report compilation deadlines with minimal supervisor guidance Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Quality checkpoint initiative — proactively flags data completeness gaps or coding errors to the project lead before they propagate into downstream analyses 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.
Applying
  1. Junior staff onboarding — guides new statistical assistants through data entry procedures, coding conventions, and database maintenance protocols 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.
  2. Project milestone accountability — takes ownership of compilation timelines for recurring statistical publications and communicates schedule risks in advance 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.
  3. Process improvement advocacy — champions revisions to data verification or file organization workflows by building consensus among team members and supervisors Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Exceeding
  1. Statistical support team leadership — coordinates task distribution, quality review cycles, and deadline management across a team of statistical assistants on large-scale data projects Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Institutional knowledge stewardship — develops succession documentation and cross-training programs ensuring continuity of critical data management functions Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Organizational methodology influence — leads adoption of updated coding standards or database structures across multiple departments or project teams O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
3Metacognition9 statements
Emerging
  1. Error self-identification — recognizes own data entry or calculation mistakes during routine self-checking before submitting work for formal review 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.
  2. Learning strategy awareness — identifies which data coding or formula application tasks require additional study or practice and seeks resources accordingly Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Developing
  1. Verification habit calibration — adjusts personal source-data checking routines based on reflection on past accuracy errors in prior data sets 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.
  2. Skill gap articulation — accurately describes to supervisors which statistical software functions or formula types require further development Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Applying
  1. Analytical approach evaluation — reflects on the appropriateness of chosen statistical formulas or data organization methods after project completion and documents lessons learned 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.
  2. Cognitive load management — monitors own performance during high-volume data entry periods and applies deliberate pacing or verification strategies to maintain accuracy Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Learning transfer assessment — evaluates whether techniques learned on one data project apply effectively to new data structures before committing to an approach Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Exceeding
  1. Metacognitive modeling — coaches junior statistical assistants to develop their own self-monitoring habits for data verification and formula selection Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Process reflection leadership — facilitates team retrospectives that examine collective analytical decision-making and surface systemic sources of data error 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.
4Critical Thinking10 statements
Emerging
  1. Source data inspection — examines incoming data sets for obvious missing values, duplicate entries, or format inconsistencies before beginning entry tasks 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.
  2. Formula selection recognition — identifies when a requested calculation requires a different statistical formula than the one initially assumed O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Developing
  1. Data anomaly investigation — traces unexpected values in compiled data back to their source records to determine whether they represent errors or legitimate outliers 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.
  2. Assumption identification — questions whether the coding scheme or data collection method underlying a data set is appropriate for the analysis being requested 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.
  3. Report logic verification — evaluates whether chart and graph representations are consistent with underlying tabular data before submission O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Applying
  1. Complex problem diagnosis — systematically isolates the cause of recurring data discrepancies by examining source forms, entry procedures, and coding instructions in sequence 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.
  2. Evidence-based recommendation — presents documented evidence of database inaccuracies to analysts with a proposed corrective course of action grounded in the data 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.
  3. Analytical method scrutiny — evaluates the statistical formulas specified in project instructions to confirm they are appropriate for the data type and analytical objective O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Exceeding
  1. Systemic data quality critique — conducts structured audits of database integrity across multiple data collections, identifying root causes and recommending preventive controls 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.
  2. Methodology challenge — presents evidence-based critiques of established data coding or compilation protocols to senior analysts and proposes validated alternatives 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.
5Collaboration9 statements
Emerging
  1. Team data task coordination — contributes assigned portions of large data entry or coding tasks on schedule so that colleagues can proceed with dependent work 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.
  2. Information sharing — communicates relevant observations about source data quality to teammates during joint data review sessions Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Developing
  1. Cross-role data handoff — coordinates smooth transfer of coded data sets and file documentation to analysts or report writers with complete and accurate handoff notes 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.
  2. Survey coordination participation — collaborates with field staff and project coordinators to organize survey forms for analysis within established timelines O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Applying
  1. Multi-team publication support — works jointly with analysts, editors, and administrators across functional teams to compile and finalize statistical publications 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.
  2. Conflict navigation in data disputes — facilitates constructive resolution when team members disagree on data interpretation or coding classification decisions Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Shared database stewardship — collaborates with IT and analyst colleagues to maintain, update, and document shared databases so all users have consistent access O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Exceeding
  1. Inter-organizational data collaboration — leads joint working groups with external partner agencies to align data formats, filing standards, and reporting timelines 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.
  2. Collaboration framework design — establishes team protocols for collaborative data review, task assignment, and quality handoff that are adopted as standard operating procedure Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
6Character9 statements
Emerging
  1. Data confidentiality adherence — handles sensitive survey responses and personal data strictly according to established privacy and access control policies 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.
  2. Deadline honesty — accurately reports progress status and potential delays on data entry tasks to supervisors without minimizing known risks Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Developing
  1. Error accountability — acknowledges and documents self-identified data entry or coding errors transparently, initiating correction without prompting 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.
  2. Objectivity in data handling — records and codes data values exactly as they appear in source documents without editing to conform to expected outcomes 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.
Applying
  1. Statistical integrity maintenance — refuses to alter, suppress, or selectively report data findings under pressure, upholding analytical objectivity in all compiled outputs 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.
  2. Professional reliability — consistently delivers accurate, complete data work products on time across extended project cycles without requiring quality supervision Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Ethical reporting practice — ensures charts, graphs, and narratives represent data findings without distortion, misleading scaling, or selective omission O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Exceeding
  1. Integrity standard modeling — sets the professional benchmark for ethical data handling and accurate reporting within the team, mentoring others on responsible statistical practice 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.
  2. Organizational trust building — earns recognition as a reliable steward of sensitive data, enabling expanded access and responsibility across high-stakes statistical projects Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
7Creativity9 statements
Emerging
  1. Alternative coding approach exploration — considers multiple valid code assignments for ambiguous data entries before selecting the most appropriate classification 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.
  2. Visualization format experimentation — tries different chart or graph types to determine which best communicates a specific statistical finding O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Developing
  1. Data organization innovation — devises improved file naming conventions or folder structures that reduce retrieval time and minimize filing errors 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.
  2. Report layout improvement — redesigns report templates to present statistical findings in a more readable and logically sequenced format O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Applying
  1. Novel data display design — creates custom charts or composite visualizations that communicate multi-variable statistical findings more clearly than standard formats allow 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.
  2. Workflow automation ideation — identifies repetitive data entry or coding steps that can be streamlined through software macros or formula automation and prototypes solutions Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Cross-source synthesis approach — develops creative methods for reconciling data from disparate source formats into a unified, analyzable structure O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
Exceeding
  1. Analytical framework innovation — introduces new approaches to data compilation and verification that improve team-wide output quality and are formally adopted into project standards 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.
  2. Publication design leadership — leads the creative redesign of recurring statistical publications to improve interpretability and stakeholder engagement with findings 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.
8Growth Mindset9 statements
Emerging
  1. Feedback receptivity — accepts supervisor corrections to data entry errors or coding decisions and applies the feedback immediately on subsequent tasks Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Skill stretch acceptance — volunteers for data tasks involving unfamiliar statistical formulas or software functions rather than defaulting to comfortable routines Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Developing
  1. Statistical software proficiency pursuit — seeks out tutorials, documentation, or colleague expertise to expand competency in data analysis software beyond current assignments 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.
  2. Error-to-learning conversion — documents recurring data mistakes and self-assigns practice exercises to address identified skill gaps 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.
Applying
  1. Sustained accuracy improvement — demonstrates measurable reduction in data error rates over successive projects by systematically applying lessons from prior feedback 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.
  2. Complexity pursuit — requests assignment to more complex data compilation or analytical support tasks as a deliberate strategy for professional development Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Methodology learning agility — adapts quickly when project requirements shift to unfamiliar data formats or statistical methods by proactively acquiring necessary knowledge 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.
Exceeding
  1. Growth culture cultivation — fosters a team environment where statistical assistants view data errors as learning opportunities and share process improvement insights openly Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Continuous improvement leadership — benchmarks team data quality metrics over time and drives iterative refinements to practice based on longitudinal performance evidence 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.
9Mindfulness9 statements
Emerging
  1. Attention anchoring during entry — maintains focused attention on source document values during data entry tasks to reduce transcription errors caused by distraction 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.
  2. Deadline awareness — monitors project timelines with consistent present-moment awareness, recognizing schedule pressure before it becomes a quality risk Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Developing
  1. Accuracy-oriented pacing — regulates work speed during high-volume data entry periods to sustain precision rather than prioritizing volume at the expense of quality 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.
  2. Emotional regulation under correction — responds to supervisor feedback on data errors with composure and constructive engagement rather than defensiveness Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Applying
  1. Intentional verification practice — applies deliberate, systematic source-data checking routines as a consistent habit rather than a reactive response to suspected errors 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.
  2. Workload stress management — maintains professional effectiveness and data accuracy standards during peak reporting periods through purposeful attention regulation strategies Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  3. Context-switching discipline — re-establishes full task focus when transitioning between different data projects or coding schemes to prevent cross-contamination of methods Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Exceeding
  1. Mindful quality culture modeling — demonstrates and teaches attention management practices to team members that reduce collective data entry error rates during sustained high-volume work Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Strategic intentionality in project planning — approaches large-scale data compilation assignments with deliberate sequencing and risk-aware decision-making that reflects ongoing situational awareness 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.
10Fortitude9 statements
Emerging
  1. Deadline persistence — continues accurate data entry work through end-of-cycle deadline pressure without reducing verification effort or accuracy standards Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Error recovery composure — resumes productive work promptly after discovering a significant data mistake, correcting it methodically rather than abandoning the task Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Developing
  1. Repetitive task endurance — sustains concentration and accuracy across extended periods of high-volume coding or data entry without degradation in output quality 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.
  2. Ambiguity tolerance — proceeds with data coding decisions under incomplete source documentation by applying established protocol logic and flagging the issue for review 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.
Applying
  1. High-stakes deadline resilience — delivers complete, accurate statistical compilations for major publication cycles under compressed timelines and competing demands 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.
  2. Data crisis navigation — maintains methodical accuracy and professional composure when discovering large-scale data integrity issues requiring extensive rework 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.
  3. Change endurance — adapts to abrupt shifts in data formats, coding schemes, or software platforms without sustained performance decline Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
Exceeding
  1. Team resilience anchoring — stabilizes team morale and maintains quality standards during high-pressure statistical reporting emergencies, modeling composure and disciplined problem-solving Pathsmith Durable SkillsPathsmith Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.
  2. Sustained institutional persistence — maintains exceptional data quality and professional standards across multi-year project cycles characterized by evolving requirements and recurring high-stakes deadlines 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.
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.
Show O*NET source anchors52 anchors · skillscrosswalk.com

O*NET enrichment · skillscrosswalk.com

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Source anchors that ground each statement

Related titles
Actuarial Analyst · Actuarial Assistant · Actuarial Technician · Actuary Clerk · Administrative Analyst · Advertising Statistical Clerk · Analytical Clerk · Bookman · Chart Calculator · Chart Changer · Chart Clerk · Compilation Clerk
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
  1. Compute and analyze data, using statistical formulas and computers or calculators.
  2. Check source data to verify completeness and accuracy.
  3. Enter data into computers for use in analyses or reports.
  4. Compile reports, charts, or graphs that describe and interpret findings of analyses.
  5. Participate in the publication of data or information.
  6. File data and related information, and maintain and update databases.
  7. Organize paperwork, such as survey forms or reports, for distribution or analysis.
  8. Code data prior to computer entry, using lists of codes.
CIP education codes
52.0302

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.