Biotech Data Scientist
Biotech Data Scientists apply machine learning to drug discovery, genomics, and personalized medicine. They work with biological data analyzing complex biological systems.
Median Salary
$170,000
Job Growth
High — biotech increasingly relies on data science
Experience Level
Entry to Leadership
Salary Progression
| Experience Level | Annual Salary |
|---|---|
| Entry Level | $115,000 |
| Mid-Level (5-8 years) | $170,000 |
| Senior (8-12 years) | $205,000 |
| Leadership / Principal | $245,000+ |
What Does a Biotech Data Scientist Do?
Biotech Data Scientists apply machine learning to accelerate drug discovery and development. They analyze genomic data identifying disease markers. They work on protein structure prediction and molecular property prediction. They identify novel drug targets using AI. They analyze clinical trial data predicting treatment outcomes. They build models for personalized medicine. They work closely with biologists translating AI into biological insights.
A Typical Day
Data analysis: Analyze genomic dataset. Identify gene mutations associated with disease.
Model development: Build ML model predicting protein structure or drug efficacy.
Validation: Validate model predictions with experimental data.
Literature: Review latest AI advances in biotech. Stay current.
Collaboration: Work with biologists interpreting results and planning next steps.
Publication: Publish results in scientific journals.
Communication: Present findings to scientific team.
Key Skills
Career Progression
Biotech data scientists often progress to head of computational biology or Chief Scientific Officer roles.
How to Get Started
Biology: Biology fundamentals—genetics, molecular biology, disease biology.
Machine learning: Strong ML skills. Often requires more sophisticated techniques than general ML.
Bioinformatics: Bioinformatics knowledge and tools.
Python/R: Expert statistical programming.
Domain: Work in biotech companies or research institutions.
Research: Conduct original research on ML applications to biology.
Collaboration: Work closely with biologists.
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What do biotech data scientists do?▼
Analyze genomic data, predict protein structure, identify drug targets, analyze clinical trial data, build prediction models for disease.
What's changing in biotech with AI?▼
Dramatically shorter drug discovery timelines. AlphaFold solved protein structure prediction. AI identifying new disease targets. Precision medicine advances.
What domain knowledge is required?▼
Biology fundamentals. Understanding genes, proteins, disease. Experiment design. Statistical rigor in clinical context.
How important is collaboration with biologists?▼
Critical. Biotech is highly interdisciplinary. Data scientists need to understand biology to ask right questions.
Is biotech data science career secure?▼
Yes. Biotech is investing heavily in AI. Career prospects are excellent.
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Last updated: 2026-03-07