Bioinformatics & Genomics

Genomics Data Scientist

Combine population-scale genomics with data science to drive biomarker discovery and precision medicine.

Build my Genomics Data Scientist roadmap Compare salaries See open roles

Genomics data scientists work with population-scale datasets — UK Biobank, All of Us, FinnGen — to find variants linked to disease, build polygenic risk scores, and feed translational pipelines. The role blends statistical genetics, large-scale data engineering, and biomedical context.

Genomics Data Scientist salary (USD)

entry

$95k–$130k

mid

$130k–$180k

senior

$180k–$260k

US base ranges blended from Levels.fyi, BLS, Glassdoor, and Payscale (2024–2025). See full salary benchmark →

What a Genomics Data Scientist does day-to-day

Run GWAS and rare-variant analyses on biobank-scale cohorts.
Build and validate polygenic risk scores for disease prediction.
Develop scalable pipelines for variant QC, imputation, and association testing.
Communicate findings to clinicians, geneticists, and drug discovery teams.

Required skills & tools

Core knowledge

Statistical geneticsPopulation geneticsCausal inferenceCloud data engineering

Tools

PLINKREGENIEHailSAIGEBOLT-LMMSparkTerra/AnVIL

Languages

PythonRSQLBash

12-month roadmap to Genomics Data Scientist

1
Statistical genetics (0–3 mo)
Hardy-Weinberg, LD, association testing, multiple testing correction.
2
Tooling (3–6 mo)
PLINK + REGENIE on 1000 Genomes, Hail on Spark.
3
Biobank work (6–9 mo)
UK Biobank or FinnGen-scale GWAS reproduction.
4
Specialty (9–12 mo)
Polygenic risk scores, Mendelian randomization, or fine-mapping.

Personalize this roadmap with AI

Job titles to target

• Statistical Geneticist
• Senior Genomics Data Scientist
• Principal Scientist, Human Genetics

Where they hire

• Pharma human genetics
• Genomics startups
• Biobank consortia
• Insurance & healthtech

FAQ

What's the difference between a genomics data scientist and a bioinformatician?

Genomics data scientists focus on population-scale statistical genetics and biobank analytics, while bioinformaticians span all omics modalities including sample-level pipelines. The former leans heavier on statistics, the latter on pipeline engineering.

Do I need access to UK Biobank to get hired?

No — you can reproduce published GWAS on open datasets like 1000 Genomes or the GTEx releases. A clear writeup of an end-to-end reproduction is enough to interview.

Is Hail or PLINK more important to learn first?

Start with PLINK to understand the underlying statistics on a single machine, then learn Hail when you move to biobank-scale data on Spark.