Bioinformatics Analyst
About This Role
Uses computational tools to analyze large biological data sets, such as genomic sequences of crops.
A Day in the Life
Bioinformatics Analysts process and interpret biological datasets from genomics, transcriptomics, and proteomics experiments — bridging wet-lab researchers and senior computational scientists to deliver actionable insights from complex data.
- Run standard bioinformatics pipelines for RNA-seq, ChIP-seq, or variant calling analysis
- Perform quality control of raw sequencing data and pre-processing steps
- Generate differential gene expression or pathway enrichment analyses
- Create visualisations (volcano plots, heatmaps, PCA plots) to communicate findings
- Maintain and document bioinformatics workflows and code repositories
- Troubleshoot pipeline failures and adapt tools to new data types
- Support researchers in data interpretation and presentation of results
- Stay current with new analytical tools and best practices in the field
Work Environment
Typical hours: 42h/week · WLB score 8/10 · OCCASIONAL overtime
Computational role with flexible hours; good WLB especially in academic or remote settings.
Skills Required
Technical Skills
Soft Skills
Tools & Software
Salary in Sri Lanka (LKR / month)
Typical progression: 3yr to mid · 6yr to senior
Bioinformatics Analyst → Senior Analyst → Bioinformatician → Computational Biology Lead
Global Salary (USD / year)
Top Markets
Market Outlook
GROWING
Growing as genomic research programs expand at Sri Lankan universities and research institutes.
Hiring: MEDIUM
GROWING
High global demand in pharma, genomics companies, agri-biotech, and academic research labs.
Entry Requirements
Sri Lanka
Preferred
Global
Preferred
Helpful Certifications
Entrepreneurship & Freelancing
Platforms (SL)
Risks & Challenges
AI Replacement Risk
MEDIUM
MID TERM
Burnout Risk
LOW
Job Security (SL)
HIGH
How to Qualify in Sri Lanka
View all paths →Reviews & Ratings
Frequently Asked Questions
Bioinformatics Analysts process and interpret biological datasets from genomics, transcriptomics, and proteomics experiments — bridging wet-lab researchers and senior computational scientists to deliver actionable insights from complex data.
