{"schemaVersion":"1.0","exportedAt":"2026-05-15T12:51:30.039Z","occupation":{"soc":"15-2051.00","title":"Data Scientists","group":"Computer & Mathematical","sector":"54","jobZone":4,"jobZoneInferred":false},"framework":{"version":"v.26.05","description":"","contextCovered":"This 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.","levels":{"emerging":{"label":"Emerging","statements":["Structured datasets and query tools — retrieve and inspect using standard SQL commands under direct supervisor guidance in a business analytics environment.","Program malfunctions and error logs — identify and document following established troubleshooting checklists on assigned data pipelines.","Business intelligence dashboards — interpret pre-built visualizations and summarize findings in written reports for team review.","Statistical software and development environments — execute provided scripts and record outputs under close mentorship during onboarding projects.","Data quality issues and anomalies — recognize and escalate using defined protocols within a structured data governance workflow.","Database management systems — navigate and perform basic queries following documented procedures on production or staging environments.","Computer program installation and configuration — assist senior staff in coordinating and testing setup steps according to written runbooks.","Technical findings and data summaries — communicate clearly in team meetings using active listening and structured speaking techniques.","Mathematical and statistical concepts — apply foundational methods such as descriptive statistics to support routine analytical tasks assigned by senior data scientists.","Cloud-based management tools — operate under direction to monitor resource usage and flag irregularities for supervisor review."]},"developing":{"label":"Developing","statements":["Recurring data pipeline malfunctions — diagnose and resolve with reduced oversight by applying systematic debugging techniques in production environments.","Business problems involving integrated data sources — analyze independently using business intelligence software to develop actionable solution recommendations.","Computer programs and automated workflows — test, maintain, and monitor on a scheduled basis, adapting procedures when standard approaches prove insufficient.","Staff and end-user data-related inquiries — address by providing clear, accurate assistance on database tools and analytical software in a service-oriented setting.","Moderately complex datasets from multiple systems — join, transform, and model using SQL and scripted environments to support departmental decision-making.","Project timelines and analytical deliverables — manage using project management software, coordinating tasks with cross-functional stakeholders independently.","Analytical findings and methodology — document in written technical reports that meet organizational standards for clarity and reproducibility.","Statistical and machine learning models — build and validate in familiar problem contexts, adjusting hyperparameters based on performance metrics.","Data storage and cloud infrastructure configurations — maintain and troubleshoot using storage networking and cloud-based management software with limited supervision.","Emerging tools and analytical techniques — evaluate through active learning and apply selectively to improve existing workflows within established team practices."]},"proficient":{"label":"Proficient","statements":["Complex, non-routine system and program malfunctions — diagnose root causes autonomously and implement durable fixes across interconnected data systems in enterprise environments.","End-to-end analytical solutions for strategic business problems — design and deliver, integrating data from disparate sources using advanced modeling and business intelligence platforms.","Machine learning and predictive models — develop, deploy, and monitor at full production scale, exercising independent judgment on algorithm selection and validation strategy.","Data infrastructure spanning databases, cloud services, and storage networks — architect and optimize to ensure reliability, performance, and security across the organization.","Ambiguous, high-stakes analytical questions — frame, investigate, and resolve by applying inductive and deductive reasoning across novel data environments.","Cross-functional teams and senior stakeholders — advise by translating complex quantitative findings into accessible recommendations through expert oral and written communication.","Procedure management and content workflow software — configure and govern to standardize data science processes and ensure consistency of analytical outputs.","Ethical, legal, and technical risks in data projects — evaluate with high attention to detail and integrity, applying cautious judgment before deployment decisions.","Custom analytical tools and automation scripts — engineer independently within development environments to accelerate team productivity on recurring research tasks.","Organizational data literacy gaps — assess and address by designing training materials and knowledge-sharing sessions that build analytical capability across user groups."]},"advanced":{"label":"Advanced","statements":["Enterprise-wide data science strategy and capability roadmap — define and champion, aligning analytical investments with long-term organizational objectives across all business units.","Organizational standards for model development, validation, and governance — establish and enforce, setting the technical direction that all data science practitioners follow.","Senior data scientists and cross-disciplinary teams — mentor and develop through structured coaching, performance feedback, and deliberate career growth planning.","Novel methodological approaches and innovative tool adoption — lead evaluation and institutionalization of, driving competitive differentiation through intellectual curiosity and calculated risk-taking.","Executive leadership and board-level stakeholders — advise by synthesizing complex analytical insights into strategic narratives that directly inform high-impact business decisions.","Partnerships with engineering, product, and domain leadership — orchestrate to embed data science solutions into core operational and product development workflows at scale.","Organizational risk posture for data, privacy, and algorithmic accountability — shape by developing policies and oversight mechanisms grounded in integrity and regulatory compliance.","Large-scale system overhauls and data platform modernizations — sponsor and govern, ensuring technical excellence and business continuity throughout multi-year transformation programs.","Talent acquisition pipelines and workforce development programs for data science — design and lead, ensuring the organization attracts and retains top-tier analytical professionals.","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."]}}},"sources":{"onet":"v30.2 (CC BY 4.0)","crosswalk":"https://skillscrosswalk.com","generator":"LER.me"},"attribution":"© EBSCOed"}