Key Responsibilities and Required Skills for Computational Biologist

🎯 Role Definition

As a Computational Biologist, you will be responsible for designing, implementing and interpreting advanced computational methods to analyse large‑scale biological data, build predictive models of biological systems and translate insights into research or product development outcomes. You will collaborate closely with biologists, data scientists, software engineers and domain experts to build pipelines, develop algorithms, visualise results and drive strategic biological decisions. Your work will enable discovery in genomics, transcriptomics, proteomics, phenotyping and systems biology in academic, biotech or pharmaceutical settings.

📈 Career Progression

Typical Career Path

Entry Point From:

Bioinformatics Analyst or Data Scientist – Life Sciences
Graduate Researcher – Computational Biology
Software Engineer with Biological Data Experience

Advancement To:

Senior Computational Biologist or Lead Bioinformatics Scientist
Principal Scientist – Computational Biology / Modelling
Director of Computational Biology or Head of Bioinformatics

Lateral Moves:

Machine Learning Engineer – Bioinformatics Tools
Systems Biology Scientist – Computational and Wet‑lab Interface
Data Science Lead – Life Sciences and Genomics Analytics

Core Responsibilities

Primary Functions

Develop, maintain and deploy computational pipelines for the analysis of large‑scale omics data including genomics, transcriptomics, proteomics and single‑cell sequencing.
Design and implement algorithms and statistical models to interpret biological phenomena, enable predictive modelling of cellular systems, disease states or therapeutic outcomes.
Integrate heterogeneous biological data (multi‑omics, imaging, clinical, phenotypic, environmental) to support discovery, decision‑making and hypothesis generation.
Collaborate with experimental biologists to design studies, refine computational requirements, interpret results, and provide actionable insights that guide wet‑lab experiments.
Build, test and optimise software tools, databases and web applications (e.g., visualisation dashboards, data portals) to make analysis results accessible to scientists and stakeholders.
Perform quality control, data cleaning, transformation, annotation and management of large biological datasets including raw sequence files, metadata and derived results.
Monitor key analytics metrics, track pipeline performance, ensure reproducibility, version control and maintain high‑quality coding/analysis practices.
Prepare clear, concise reports, visualisations and presentations for internal or external audiences, including scientists, product teams or senior leadership.
Stay abreast of advances in computational biology, machine learning, bioinformatics tools, high‑performance computing and relevant research literature; propose innovation in methods and workflows.
Lead or support the writing of scientific publications, patents or regulatory documents summarising computational biology findings.
Validate and benchmark new computational approaches, ensure pipelines meet performance criteria, and document method validation/verification steps.
Work with cloud computing platforms, high‑performance computing clusters and scalable infrastructure to support computational biology workloads.
Engage in cross‑functional meetings with engineering, product, regulatory, clinical or business units to align computational biology strategy with organisational goals.
Manage and optimise database infrastructure and biological data repositories, ensure data security, metadata standards and compliance with relevant regulations.
Mentor junior computational biologists, analysts or interns; conduct code review, share best practices and foster a culture of scientific excellence and collaboration.
Identify new opportunities for applying computational biology in commercial or research contexts, evaluate feasibility, provide recommendations and contribute to strategic planning.
Oversee the development of machine learning or AI models tailored to biological datasets and experimental questions, and integrate those models into computational workflows.
Participate in vendor evaluations, software tool selections, open‑source code contributions and community platforms that support computational biology innovations.
Ensure compliance with best practices for reproducible research, open‑source code release, data sharing, documentation and version control across computational biology initiatives.
Support exploratory data analysis, hypothesis generation and prototype workflows to uncover new biological insights, optimise processes and enhance discovery throughput.

Secondary Functions

Support ad‑hoc data requests and exploratory data‑analysis of biological datasets to inform strategic decision‑making and support research teams.
Contribute to the organisation’s computational biology strategy and roadmap — providing insight on future tools, data platforms and analytical capabilities.
Collaborate with product development or engineering teams to translate computational biology insights into product‑features, software modules or data‑driven tools and participate in agile or sprint planning ceremonies.

Required Skills & Competencies

Hard Skills (Technical)

Proficiency in programming languages such as Python, R (or equivalent) for computational biology and data science workflows.
Deep understanding of bioinformatics methods: sequence alignment, genome assembly, variant calling, transcriptomics, proteomics, single‑cell or multi‑omics data analysis.
Experience developing and implementing machine learning, statistical modelling and predictive analytics on biological datasets.
Working knowledge of data pipelines, HPC or cloud infrastructure, workflow management systems and scalable architecture for biological data.
Competence in database design, data integration, metadata management, data warehousing and biological data repositories.
Experience in software development practices: code version control, unit testing, documentation, peer review and tool packaging for distribution.
Ability to visualise, interpret and communicate complex biological results via data visualisation tools, dashboards or reports.
Understanding of biological domains (genetics, cell biology, molecular biology, disease biology) to translate analysis into relevant biological insight.
Knowledge of regulatory and research compliance aspects such as data sharing standards, open‑source software licensing, reproducible research practices.
Skilled in collaborating across disciplines — ability to partner with wet‑lab scientists, engineers, product teams and business stakeholders to produce impactful outcomes.

Soft Skills

Excellent written and verbal communication: able to articulate complex computational biology findings to scientific and non‑scientific stakeholders.
Strong analytical and problem‑solving mindset: capable of troubleshooting pipelines, interpreting biological data and deriving actionable insights.
High attention to detail: essential when building accurate algorithms, managing large datasets and ensuring reproducible results.
Effective collaboration and team‑orientation: comfortable working in multi‑disciplinary teams and contributing to shared goals.
Time‑management and organisation: able to handle multiple projects, manage priorities, meet deadlines and deliver high‑quality work.
Curiosity and continuous‑learning mindset: passionate about staying current with emerging technologies, methodologies and biological research.
Adaptability and flexibility: capable of adjusting to evolving project requirements, new data types or shifting priorities.
Strategic thinking: able to align computational biology efforts with broader organisational or research strategy, propose enhancements and drive innovations.
Leadership and mentoring: able to guide junior colleagues, influence teams and foster a culture of excellence and knowledge sharing.
Integrity and accountability: committed to data integrity, ethical research practices and transparent documentation.

Education & Experience

Educational Background

Minimum Education:

Master’s degree in Computational Biology, Bioinformatics, Computer Science, Biostatistics, or a related quantitative life‑sciences discipline.

Preferred Education:

PhD in Computational Biology, Bioinformatics, Systems Biology or a related field, ideally with peer‑reviewed publications and experience with large‑scale biological datasets.

Relevant Fields of Study:

Computational Biology
Bioinformatics
Computer Science
Statistics / Biostatistics
Genomics
Systems Biology

Experience Requirements

Typical Experience Range:

2‑5 years of experience in computational biology, bioinformatics, or related analytical role within academia, biotech or pharmaceutical industries.

Preferred:

5+ years of experience in designing and leading computational biology projects, managing large‑scale data pipelines, published work, and cross‑functional collaborations with biological discovery or development teams.