Clinical Data Scientist

Unlearn•San Francisco, CA

About The Position

Clinical Data Scientists at Unlearn build world-class data products that underpin advances in machine learning for clinical research. Working in close collaboration with machine learning scientists, engineers, clinical scientists, and product partners, they transform complex clinical data into high-quality, well-understood datasets tailored for modeling and other downstream applications. The role spans multiple disease areas and data sources, requiring strong software practices, careful data curation, and the ability to quickly develop clinical domain expertise. In addition to internal development, Clinical Data Scientists often work in forward-deployed settings, partnering directly with clients to ensure Unlearn’s solutions are appropriately tailored to real-world study designs.

Requirements

BS or advanced degree in Bioinformatics, Computer Science, Psychology, Public Health, or related discipline.
2+ years of experience wrangling data from disparate sources, data cleaning, and harmonizing datasets for analysis.
Experience working with complex or nuanced datasets.
Fluency in Python and its essential data science tools (numpy, pandas).
Demonstrated experience in collaborative software development.
Experience with clinical datasets in applied machine learning applications.
Experience working in multidisciplinary teams that include scientists, engineers, and product management.

Nice To Haves

Prior experience in forward-deployed, client-facing settings.
Experience in clinical/healthcare data.

Responsibilities

Transform clinical trial, observational, and electronic health record data into high-quality, well-structured datasets.
Analyze longitudinal clinical datasets, developing a strong understanding of outcome measures, biomarkers, and other variables commonly used in clinical research across multiple disease areas.
Collaborate closely with cross-functional teams of data scientists, machine learning scientists, engineers, and clinicians to design, build, and maintain robust data products grounded in sound data organization, domain knowledge, and careful analysis.
Communicate technical findings, data characteristics, and limitations clearly and effectively to both internal partners and external collaborators, including in forward-deployed, client-facing settings.