Staff Scientist - Causal Inference
Recursion
Location
USA
Type
Full-time
Posted
Jan 13, 2026
Compensation
USD 200000 – 200000
Mission
What you will drive
Core responsibilities:
- Apply statistical programming skills to large databases, including health insurance claims, electronic health records, and real-world human genomics data
- Apply causal inference skills to infer relationships between clinical, genomics, and outcomes
- Build relationships with clinical development, clinical operations, biometrics and computational biology teams
- Produce scientifically rigorous evidence that will facilitate decision-making in clinical (and sometimes pre-clinical) programs
Impact
The difference you'll make
This role creates positive change by dramatically increasing the probability of success of clinical programs through applying technology to increase efficiency in clinical development, ultimately advancing the future of medicine to radically improve lives.
Profile
What makes you a great fit
Required skills and experience:
- Advanced degree in a quantitative discipline (alternative disciplines considered with quantitative work experience)
- At least 6 years of experience in applying relevant skills
- Expertise with at least one statistical programming language such as Python or R
- Strong background in applied statistics, including model fitting and inference from observational data, with hands-on experience developing and validating machine-learning predictive models
- Experience in biotech research and development
Benefits
What's in it for you
Compensation and benefits:
- Estimated annual base salary range: $200,600 to $238,400
- Eligible for annual bonus and equity compensation
- Comprehensive benefits package
- Values-driven culture emphasizing integrity, collaboration, urgency, and continuous learning
About
Inside Recursion
Recursion is a clinical stage TechBio company leading the space by decoding biology to radically improve lives through its Recursion OS platform, which generates one of the world's largest proprietary biological and chemical datasets and uses machine learning to advance the future of medicine.