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Data Scientists

SOC 15-2051.00Job Zone 4 · Considerable Preparationv.26.05

Context coveredThis framework covers data science practice in enterprise and technology-driven organizations, spanning exploratory analysis, model development, data infrastructure management, stakeholder communication, and organizational leadership across Job Zone 4 career stages.

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. Structured datasets and query tools — retrieve and inspect using standard SQL commands under direct supervisor guidance in a business analytics environment.
  2. Program malfunctions and error logs — identify and document following established troubleshooting checklists on assigned data pipelines.
  3. Business intelligence dashboards — interpret pre-built visualizations and summarize findings in written reports for team review.
  4. Statistical software and development environments — execute provided scripts and record outputs under close mentorship during onboarding projects.
  5. Data quality issues and anomalies — recognize and escalate using defined protocols within a structured data governance workflow.
  6. Database management systems — navigate and perform basic queries following documented procedures on production or staging environments.
  7. Computer program installation and configuration — assist senior staff in coordinating and testing setup steps according to written runbooks.
  8. Technical findings and data summaries — communicate clearly in team meetings using active listening and structured speaking techniques.
  9. Mathematical and statistical concepts — apply foundational methods such as descriptive statistics to support routine analytical tasks assigned by senior data scientists.
  10. Cloud-based management tools — operate under direction to monitor resource usage and flag irregularities for supervisor review.
Developing
Mid-level / Established
  1. Recurring data pipeline malfunctions — diagnose and resolve with reduced oversight by applying systematic debugging techniques in production environments.
  2. Business problems involving integrated data sources — analyze independently using business intelligence software to develop actionable solution recommendations.
  3. Computer programs and automated workflows — test, maintain, and monitor on a scheduled basis, adapting procedures when standard approaches prove insufficient.
  4. Staff and end-user data-related inquiries — address by providing clear, accurate assistance on database tools and analytical software in a service-oriented setting.
  5. Moderately complex datasets from multiple systems — join, transform, and model using SQL and scripted environments to support departmental decision-making.
  6. Project timelines and analytical deliverables — manage using project management software, coordinating tasks with cross-functional stakeholders independently.
  7. Analytical findings and methodology — document in written technical reports that meet organizational standards for clarity and reproducibility.
  8. Statistical and machine learning models — build and validate in familiar problem contexts, adjusting hyperparameters based on performance metrics.
  9. Data storage and cloud infrastructure configurations — maintain and troubleshoot using storage networking and cloud-based management software with limited supervision.
  10. Emerging tools and analytical techniques — evaluate through active learning and apply selectively to improve existing workflows within established team practices.
Proficient
Senior / Expert IC
  1. Complex, non-routine system and program malfunctions — diagnose root causes autonomously and implement durable fixes across interconnected data systems in enterprise environments.
  2. End-to-end analytical solutions for strategic business problems — design and deliver, integrating data from disparate sources using advanced modeling and business intelligence platforms.
  3. Machine learning and predictive models — develop, deploy, and monitor at full production scale, exercising independent judgment on algorithm selection and validation strategy.
  4. Data infrastructure spanning databases, cloud services, and storage networks — architect and optimize to ensure reliability, performance, and security across the organization.
  5. Ambiguous, high-stakes analytical questions — frame, investigate, and resolve by applying inductive and deductive reasoning across novel data environments.
  6. Cross-functional teams and senior stakeholders — advise by translating complex quantitative findings into accessible recommendations through expert oral and written communication.
  7. Procedure management and content workflow software — configure and govern to standardize data science processes and ensure consistency of analytical outputs.
  8. Ethical, legal, and technical risks in data projects — evaluate with high attention to detail and integrity, applying cautious judgment before deployment decisions.
  9. Custom analytical tools and automation scripts — engineer independently within development environments to accelerate team productivity on recurring research tasks.
  10. Organizational data literacy gaps — assess and address by designing training materials and knowledge-sharing sessions that build analytical capability across user groups.
Advanced
Lead / Principal / Executive
  1. Enterprise-wide data science strategy and capability roadmap — define and champion, aligning analytical investments with long-term organizational objectives across all business units.
  2. Organizational standards for model development, validation, and governance — establish and enforce, setting the technical direction that all data science practitioners follow.
  3. Senior data scientists and cross-disciplinary teams — mentor and develop through structured coaching, performance feedback, and deliberate career growth planning.
  4. Novel methodological approaches and innovative tool adoption — lead evaluation and institutionalization of, driving competitive differentiation through intellectual curiosity and calculated risk-taking.
  5. Executive leadership and board-level stakeholders — advise by synthesizing complex analytical insights into strategic narratives that directly inform high-impact business decisions.
  6. Partnerships with engineering, product, and domain leadership — orchestrate to embed data science solutions into core operational and product development workflows at scale.
  7. Organizational risk posture for data, privacy, and algorithmic accountability — shape by developing policies and oversight mechanisms grounded in integrity and regulatory compliance.
  8. Large-scale system overhauls and data platform modernizations — sponsor and govern, ensuring technical excellence and business continuity throughout multi-year transformation programs.
  9. Talent acquisition pipelines and workforce development programs for data science — design and lead, ensuring the organization attracts and retains top-tier analytical professionals.
  10. Firm-wide culture of evidence-based decision-making — cultivate by modeling rigorous critical thinking and championing data-driven practices at every level of the organization.

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.

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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.
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Show O*NET source anchors42 anchors · skillscrosswalk.com

O*NET enrichment · skillscrosswalk.com

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Source anchors that ground each statement

Related titles
Analytics Consultant · Applied Scientist · Data Analyst · Data Analytic Scientist · Data Analytics Manager · Data Analytics Scientist · Data Analytics Specialist · Data Architect · Data Consultant · Data Economist · Data Engineer · Data Management Scientist
RAPIDS apprenticeships
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O*NET skills
Critical ThinkingReading ComprehensionActive ListeningComplex Problem SolvingSpeakingJudgment and Decision MakingWritingActive Learning
Knowledge domains
Computers and ElectronicsEnglish LanguageMathematicsEngineering and TechnologyCustomer and Personal ServiceAdministration and ManagementEducation and TrainingDesign
Abilities
Written ComprehensionOral ComprehensionDeductive ReasoningOral ExpressionInductive ReasoningInformation Ordering
Work styles
Attention to DetailDependabilityIntellectual CuriosityIntegrityCautiousnessInnovation
Technology
Business intelligence and data analysis softwareData base user interface and query softwareStorage networking softwareCloud-based management softwareProcedure management softwareData base management system softwareDevelopment environment softwareIndustrial control softwareProject management softwareContent workflow software
Tasks · seed anchors for statements
  1. Troubleshoot program and system malfunctions to restore normal functioning.
  2. Provide staff and users with assistance solving computer-related problems, such as malfunctions and program pr
  3. Test, maintain, and monitor computer programs and systems, including coordinating the installation of computer
  4. Use the computer in the analysis and solution of business problems, such as development of integrated producti
CIP education codes
11.010211.010311.070111.080211.080426.110326.110426.119927.010127.030127.030327.030427.030527.030627.039927.050127.050227.050327.060127.999930.080130.300130.390130.480130.700130.709930.710130.710230.710330.710430.719940.051245.060352.130152.1302

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.