Computational Bioinformatics

Career Paths

Bioinformatics Career Paths

This page gives a quick overview of roles, skills, and entry points in computational biology and bioinformatics.

Roles you might see

  • Bioinformatics Analyst
    • Focus: data cleaning, pipelines, and reports.
    • Typical tools: Python, R, Bash, SQL, workflow tools.
  • Computational Biologist
    • Focus: algorithm development, research modeling, and analysis.
    • Typical tools: Python, R, statistics, machine learning basics.
  • Genomics Data Scientist
    • Focus: high-throughput sequencing, ML, and reproducible analytics.
    • Typical tools: Python, R, ML libraries, cloud platforms.
  • Bioinformatics Software Engineer
    • Focus: production software, APIs, and data systems.
    • Typical tools: Python, Go, Java, Docker, CI/CD.
  • Clinical Genomics Specialist
    • Focus: variant interpretation, regulated pipelines, clinical reporting.
    • Typical tools: standards (HGVS), ClinVar, pipelines, QC.

Core skills to build in this course

  • Sequence alignment, assembly, and annotation basics.
  • Data handling and reproducible analysis with notebooks.
  • Clear reporting: methods, results, and limitations.
  • Ethical data use, privacy, and bias awareness.

How to prepare

  • Build a small portfolio: 2 to 3 notebooks or reports.
  • Practice writing short analysis summaries for non-technical readers.
  • Learn the command line and Git basics.

Where to look for roles

  • Academic labs and research institutes.
  • Biotech and pharma companies.
  • Hospitals and clinical genomics labs.
  • Government or public health agencies.

Example job boards

Suggested class activity

  • Find one job posting and map the required skills to course topics.