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Biostatisticians

SOC 15-2041.01Job Zone 5 · Extensive Preparationv.26.05

Context coveredThis framework covers biostatistical practice across pharmaceutical clinical trials, academic medical research, epidemiological studies, and regulatory submissions, calibrated to Job Zone 5 advanced-degree practitioners.

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 tables — compute and interpret under faculty or senior biostatistician supervision for assigned clinical or survey datasets.
  2. Statistical analysis software (SAS, R, or Python) — execute pre-written program code and verify output under guidance in a research computing environment.
  3. Sample size calculations — apply standard formulas to estimate requirements for straightforward clinical study designs with direction from a senior statistician.
  4. Longitudinal and cross-sectional data structures — distinguish and describe appropriate analytical approaches during structured research team meetings.
  5. Graphs and tables for clinical data — prepare draft visualizations using spreadsheet or graphics software in accordance with sponsor or journal style guidelines.
  6. Peer-reviewed biostatistics literature — read and summarize recent methodological articles to support team awareness of current analytical developments.
  7. Statistical analysis plans (SAPs) — contribute assigned sections to draft documents under close review by a senior biostatistician.
  8. Data quality and anomalies — identify and flag inconsistencies in clinical or survey datasets during routine data-cleaning tasks.
  9. Research protocols — review assigned sections to extract analytical requirements under the direction of the lead study statistician.
  10. Regulatory and ethical standards for data handling — follow established protocols for data privacy and integrity in a pharmaceutical or academic research setting.
Developing
Mid-level / Established
  1. Logistic regression and mixed-effects models — implement and interpret independently for moderately complex clinical or epidemiological datasets with periodic peer review.
  2. Statistical program code — write, test, and document analysis scripts in R or SAS for assigned study objectives in a collaborative research environment.
  3. Clinical study sample size requirements — calculate and justify estimates for standard Phase II or III trial designs, presenting assumptions to the research team.
  4. Analysis plans and methods sections — draft complete, detailed SAPs for research protocols and revise based on feedback from principal investigators.
  5. Predictive conclusions from model outputs — draw and communicate data-driven inferences to cross-functional teams including clinicians and life scientists.
  6. Model-building and variable selection techniques — apply stepwise, LASSO, or information-criterion approaches for multivariable analyses in observational studies.
  7. Tables and figures for study reports — produce publication-quality displays that comply with regulatory submission or manuscript formatting standards.
  8. Professional conferences and methodology workshops — attend and synthesize emerging approaches in biostatistics and pharmacology for internal team knowledge sharing.
  9. Collaborative study design — contribute statistical expertise to protocol development discussions with physicians and life scientists on multi-site research projects.
  10. Version-controlled code repositories — manage analysis scripts using Git or equivalent file-versioning software within a shared research infrastructure.
Proficient
Senior / Expert IC
  1. Complex longitudinal and survival analyses — design, execute, and interpret independently across the full analytical scope of Phase I–IV clinical trials or large population-based studies.
  2. Advanced model-building strategies — select and validate mixed-effect, Bayesian, or machine-learning approaches for non-routine biostatistical challenges in drug development or public health research.
  3. Comprehensive statistical analysis plans — author end-to-end SAPs for multi-arm, adaptive, or complex observational study designs without supervisory review.
  4. Sample size and power determinations for novel designs — derive requirements for adaptive, platform, or basket trial designs, accounting for multiple endpoints and interim analyses.
  5. Quantitative predictions and regulatory conclusions — synthesize statistical evidence and articulate actionable findings for FDA submissions, IRB reports, or peer-reviewed publications.
  6. Custom analytical software tools — develop and validate reproducible R packages, SAS macros, or Python modules that extend team analytical capabilities across multiple projects.
  7. Data mining and integrated database queries — extract, link, and harmonize data from electronic health records, claims, or genomic databases to support research objectives.
  8. Methodological literature — critically evaluate and adapt novel statistical methods from current publications for immediate application in ongoing research programs.
  9. Cross-disciplinary study design — lead collaborative protocol development with clinicians, pharmacologists, and regulatory scientists to ensure statistical rigor from conception through dissemination.
  10. Scientific writing and oral presentation — produce high-quality manuscripts, technical reports, and conference presentations that communicate complex biostatistical findings to diverse audiences.
Advanced
Lead / Principal / Executive
  1. Organizational biostatistics strategy — define methodological standards, tool selection, and analytical governance frameworks across an enterprise research portfolio or academic department.
  2. Emerging quantitative methods — evaluate, pilot, and institutionalize novel approaches (e.g., estimands, real-world evidence frameworks, causal inference methods) to advance the organization's scientific capabilities.
  3. Junior and mid-level biostatisticians — mentor, train, and evaluate staff through structured coaching, code review, and professional development planning in a large research organization.
  4. Regulatory strategy and agency interactions — lead statistical discussions with FDA, EMA, or comparable bodies, representing the organization's analytical positions in pre-submission and advisory meetings.
  5. Cross-functional research partnerships — establish and steward collaborative relationships with clinical development, epidemiology, bioinformatics, and commercial teams to align statistical methodology with business and scientific objectives.
  6. Grant and contract proposals — provide strategic statistical leadership for large multi-site NIH, industry, or government funding applications, ensuring scientific rigor and competitive differentiation.
  7. Institutional data infrastructure and systems — oversee design and evaluation of enterprise analytical platforms, database environments, and reproducible research pipelines at organizational scale.
  8. Publication and dissemination programs — set quality and integrity standards for statistical content across all manuscripts, regulatory dossiers, and public-facing scientific communications.
  9. Field-level thought leadership — represent the organization at national conferences, editorial boards, and professional societies, shaping methodological discourse in biostatistics and life sciences.
  10. Ethical and compliance culture — champion principles of statistical integrity, transparency in reporting, and responsible data use across research teams and institutional governance structures.

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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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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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O*NET enrichment · skillscrosswalk.com

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Source anchors that ground each statement

Related titles
Bioinformatics Scientist · Biomathematician · Biometrician · Biostatistical Consultant · Biostatistician · Clinical Biostatistician · NGS Biostatistician (Next-Generation Sequencing) · Postdoctoral Fellow · Research Biostatistician · Research Scientist · Statistical Programmer · Statistical Scientist
RAPIDS apprenticeships
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O*NET skills
MathematicsCritical ThinkingActive LearningReading ComprehensionComplex Problem SolvingScienceJudgment and Decision MakingSpeakingActive ListeningWritingProgrammingLearning StrategiesSystems AnalysisInstructingSystems EvaluationTime ManagementMonitoringCoordinationOperations Analysis
Knowledge domains
MathematicsEnglish LanguageComputers and ElectronicsMedicine and Dentistry
Abilities
Mathematical ReasoningInductive ReasoningDeductive ReasoningWritten ComprehensionOral ExpressionOral ComprehensionInformation OrderingProblem SensitivitySpeech ClarityWritten Expression
Work styles
Intellectual CuriosityAttention to DetailDependabilityAchievement OrientationIntegrityCautiousness
Technology
Operating system softwareObject or component oriented development softwareData base user interface and query softwareData mining softwareAnalytical or scientific softwareEnterprise application integration softwareFile versioning softwareGraphics or photo imaging softwareWeb platform development softwareSpreadsheet software
Tasks · seed anchors for statements
  1. Draw conclusions or make predictions, based on data summaries or statistical analyses.
  2. Analyze clinical or survey data, using statistical approaches such as longitudinal analysis, mixed-effect modeling, logistic regression analyses, and model-building techniques.
  3. Write detailed analysis plans and descriptions of analyses and findings for research protocols or reports.
  4. Calculate sample size requirements for clinical studies.
  5. Read current literature, attend meetings or conferences, and talk with colleagues to keep abreast of methodological or conceptual developments in fields such as biostatistics, pharmacology, life sciences, and social sciences.
  6. Design research studies in collaboration with physicians, life scientists, or other professionals.
  7. Prepare tables and graphs to present clinical data or results.
  8. Write program code to analyze data with statistical analysis software.
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.