Data Scientist III

Overview

Fred Hutchinson Cancer Center is an independent, nonprofit organization providing adult cancer treatment and groundbreaking research focused on cancer and infectious diseases. Based in Seattle, Fred Hutch is the only National Cancer Institute-designated cancer center in Washington.

With a track record of global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy and COVID-19 vaccines, Fred Hutch has earned a reputation as one of the world’s leading cancer, infectious disease and biomedical research centers. Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states.

Together, our fully integrated research and clinical care teams seek to discover new cures to the world’s deadliest diseases and make life beyond cancer a reality.

At Fred Hutch we value collaboration, compassion, determination, excellence, innovation, integrity and respect. Our mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems.

The Translational Data Scientist III develops, curates, and analyzes multimodal research datasets that integrate clinical, genomic, and other translational data modalities. This role focuses on building analytically ready datasets and supporting collaborative translational research projects under the guidance of senior scientific and technical leadership.

Working closely with the Translational Data Science Staff Scientist, this position contributes to data harmonization, cohort construction, and cross-domain integration using institutional data platforms and modern data engineering practices. The role emphasizes technical development, structured learning, and applied collaboration with research teams and program level efforts.

This is a hands‑on technical role situated at the interface of translational science, data engineering, and research collaboration. This role builds data science solutions, applying LLMs/AI to process, structure, and contextualize health data, and creating data products that are customized to the needs of our translational research programs at Fred Hutch including Clinical trials, Precision Oncology, disease-focused programs, and new data science capabilities both at Fred Hutch and across institutions via the Cancer AI Alliance.

At Fred Hutchinson Cancer Center, all employees are expected to demonstrate a commitment to our values of collaboration, compassion, determination, excellence, innovation, integrity, and respect.

• Identify and integrate disparate data sources, both internal and external, including clinical data, genomic data, imaging-derived data, and well-established, publicly available databases.
• Develop and deploy machine learning algorithms, predictive models, and classification methods to advance cancer research and inform clinical decision‑making, applying reproducible data processing practices within cloud‑based analytic environments.
• Deliver novel, data‑driven insights to improve outcomes in the treatment of cancer, supporting cohort definition, feature engineering, and dataset standardization.
• Identify areas of growth for the data science initiative and actively engage in enhancing the breadth and reach of data science across the Fred Hutch campus.
• Collaborate with faculty collaborators, researchers and clinicians to identify high‑impact opportunities for data science applications, translating research questions into structured data products and tools.
• Manage data science projects from creation to completion, following established practices for data security, privacy, and compliance.
• Communicate results to technical and non‑technical audiences, contributing to documentation of datasets, assumptions, and transformation logic.

• Master’s or PhD degree in Bioinformatics, Statistics, Biostatistics, Mathematics, Computer…