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Bioinformatics Technicians

SOC 15-2099.01Job Zone 4 · Considerable Preparationv.26.05

Context coveredThis framework covers bioinformatics technician practice across research laboratories, clinical genomics environments, and computational biology units in academic, healthcare, and industry settings, spanning database development, data analysis pipelines, software extension, and cross-disciplinary scientific communication.

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. Bioinformatics data — retrieve and organize from genomic, protein sequence, or gene expression databases under direct supervision in a research laboratory setting.
  2. Statistical software packages — apply to basic bioinformatics datasets following established protocols and step-by-step guidance from senior staff.
  3. Quality checks — perform routine validation of data inputs using prescribed checklists to flag anomalies in structured pipeline workflows.
  4. Structural and mutation databases — enter and retrieve records accurately using standard query templates in a supervised computational biology environment.
  5. Scientific literature — read and summarize relevant papers on emerging bioinformatics methods to support team awareness activities.
  6. Existing database queries — execute and document results under guidance to support ongoing sequence management tasks in a shared research environment.
  7. File versioning software — use to track changes to scripts and data files following team-defined version control conventions.
  8. Research team meetings — attend and actively listen to clarify data needs and programming requirements as directed by project leads.
  9. Basic programming scripts — write and test simple code in a supervised development environment to automate repetitive bioinformatics tasks.
  10. Draft report sections — contribute factual summaries of data retrieval results to scientific publications under close editorial review by senior researchers.
Developing
Mid-level / Established
  1. Bioinformatics data pipelines — analyze and manipulate datasets using statistical applications and data mining techniques with reduced oversight in a production research environment.
  2. Existing software programs and web-based tools — extend functionality as sequence management needs evolve, applying object-oriented development practices in a collaborative lab setting.
  3. Genomic and protein sequence databases — query and cross-reference multiple repositories independently to retrieve information relevant to ongoing research projects.
  4. Data quality analyses — conduct systematic assessments of inputs and model predictions, documenting findings and recommending corrective actions to project supervisors.
  5. Searchable bioinformatics databases — develop and maintain applications that process biological data for analysis and presentation in a multi-user research computing environment.
  6. Computational methods literature — monitor and evaluate new tools and technologies, summarizing implications for team workflows in regular knowledge-sharing sessions.
  7. Researchers and clinicians — confer with to gather data requirements and translate them into database schemas and analytical scripts within an interdisciplinary project team.
  8. Analytical or scientific software — configure and calibrate for specific biological analysis tasks, troubleshooting unexpected outputs in established pipeline contexts.
  9. Scientific reports and manuscripts — prepare structured drafts of methods and results sections, incorporating statistical outputs and database citations for peer review.
  10. Development environment software — manage project dependencies, testing environments, and code repositories to support reproducible bioinformatics analyses across team members.
Proficient
Senior / Expert IC
  1. Complex bioinformatics datasets — design and execute end-to-end analytical workflows using advanced statistical applications and custom data mining approaches across the full project lifecycle.
  2. Custom software extensions — architect and implement modifications to existing platforms and interactive web tools to address evolving sequence analysis requirements without external guidance.
  3. Emerging computational technologies — critically evaluate and pilot new methods, producing formal assessments that inform technology adoption decisions for the research group.
  4. Multi-source data quality assurance — lead rigorous validation of heterogeneous biological data inputs and predictive model outputs, resolving discrepancies through root-cause analysis.
  5. Enterprise bioinformatics database applications — develop, optimize, and sustain production systems that integrate genomic, expression, and mutation data for cross-functional analytical use.
  6. Cross-disciplinary stakeholders — independently facilitate technical consultations with researchers, clinicians, and IT staff to align data architecture with complex scientific objectives.
  7. Non-routine analytical problems — diagnose and resolve novel computational or data integrity challenges by applying deductive and inductive reasoning within high-stakes research settings.
  8. Scientific publications — lead the writing and revision of methods, results, and supplementary computational sections for submission to peer-reviewed journals.
  9. Systems analysis — evaluate end-to-end bioinformatics workflows to identify bottlenecks, redundancies, and failure points, implementing evidence-based improvements in production environments.
  10. Mathematical and statistical models — apply advanced quantitative reasoning to interpret biological data patterns and validate model assumptions within genome-scale analytical projects.
Advanced
Lead / Principal / Executive
  1. Organizational bioinformatics strategy — define and champion a multi-year computational roadmap aligned with institutional research priorities and emerging life-sciences technology trends.
  2. Bioinformatics competency development — mentor and evaluate technicians and analysts across all experience levels, designing training curricula that build organizational analytical capability.
  3. Enterprise-scale data architecture — oversee the design and governance of integrated genomic, proteomic, and expression database ecosystems serving institution-wide research and clinical programs.
  4. Cross-functional innovation initiatives — lead cross-departmental teams to pioneer novel computational methodologies, translating research insights into scalable bioinformatics products and services.
  5. Research computing investments — advise executive and funding stakeholders on technology acquisition, tool prioritization, and resource allocation for large-scale bioinformatics infrastructure.
  6. Quality and compliance frameworks — establish organization-wide standards for data quality, reproducibility, and regulatory compliance across all bioinformatics pipelines and publications.
  7. Strategic partnerships — cultivate collaborations with external research institutions, technology vendors, and funding agencies to advance the organization's computational biology capabilities.
  8. High-impact scientific publications — direct and contribute expert authorship to landmark manuscripts, reviews, and data resource papers that shape the field's methodological standards.
  9. Systems-level risk evaluation — assess institutional vulnerabilities in computational workflows and data security, designing mitigation strategies that safeguard sensitive biological research assets.
  10. Thought leadership — represent the organization at scientific conferences, expert panels, and policy forums, shaping community standards for bioinformatics practice and data sharing.

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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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Source anchors that ground each statement

Related titles
Bioinformatics Analyst · Bioinformatics Research Technician · Bioinformatics Specialist · Bioinformatics Technician · Biometrics Technician · Biotechnician · Data Analyst · Data Technician · Database Technician · Field Data Technician · Log Data Technician · Museum Informatics Specialist
RAPIDS apprenticeships
—
O*NET skills
Reading ComprehensionActive LearningComplex Problem SolvingActive ListeningWritingCritical ThinkingJudgment and Decision MakingSpeakingMathematicsSystems AnalysisMonitoringScienceProgrammingSystems EvaluationTime Management
Knowledge domains
Computers and ElectronicsMathematicsEnglish LanguageBiology
Abilities
Written ComprehensionOral ComprehensionDeductive ReasoningInductive ReasoningInformation OrderingWritten ExpressionMathematical ReasoningSelective AttentionOral ExpressionNear Vision
Work styles
Attention to DetailIntellectual CuriosityDependabilityAdaptabilityInnovationCautiousness
Technology
File versioning softwareEnterprise application integration softwareAccess softwareAnalytical or scientific softwareDevelopment environment softwareObject or component oriented development softwareCustomer relationship management CRM softwareEnterprise resource planning ERP softwareGeographic information systemWeb platform development software
Tasks · seed anchors for statements
  1. Analyze or manipulate bioinformatics data using software packages, statistical applications, or data mining techniques.
  2. Extend existing software programs, web-based interactive tools, or database queries as sequence management and analysis needs evolve.
  3. Maintain awareness of new and emerging computational methods and technologies.
  4. Conduct quality analyses of data inputs and resulting analyses or predictions.
  5. Enter or retrieve information from structural databases, protein sequence motif databases, mutation databases, genomic databases or gene expression databases.
  6. Develop or maintain applications that process biologically based data into searchable databases for purposes of analysis, calculation, or presentation.
  7. Confer with researchers, clinicians, or information technology staff to determine data needs and programming requirements and to provide assistance with database-related research activities.
  8. Participate in the preparation of reports or scientific publications.
CIP education codes
26.110426.119927.010127.030127.030327.030427.030527.030627.039927.999930.080130.300130.490130.5001

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.