Sr Staff Scientist - Data

Fred Hutchinson Cancer Center•Seattle, WA

About The Position

The Sr Staff Scientist identifies, evaluates, and advances technology opportunities that support translational research and data-driven discovery. This role works in close partnership with OCDO infrastructure and data engineering leadership to identify technical solutions to meet emerging translational needs, assess potential data and AI technologies, and guide the early development of technical solutions related to our multimodal patient data platform. This role works in direct collaboration with the OCDO Data Science Staff Scientist to evaluate feasibility of translational data projects at the programmatic level that require integration across infrastructure and data analysis to ensure complete solutions that meet the needs of Clinical Trials, Precision Oncology, and disease-focused translational programs integrating clinical and research efforts. The position focuses on discovery and evaluation of translational research needs, assessment of emerging technologies, and coordination with internal and external partners to frame viable implementation pathways for complex data and technology projects. This is a technically grounded role that bridges translational research use cases with institutional data platforms, cloud infrastructure, and security and governance considerations.

Requirements

PhD in bioinformatics, computational biology, biomedical informatics, genomics, systems biology, biomedical engineering, or a related quantitative biomedical field.
Minimum 5 years of experience working with biomedical or translational research data in an academic medical center, research institute, or equivalent data-intensive research environment.

Nice To Haves

Demonstrated expertise in bioinformatics, scientific computing, or computational analysis of biomedical data, including experience working with large and complex research datasets.
Strong understanding of modern data infrastructure, including cloud-based research environments, data platforms, and data engineering concepts.
Experience with AWS and/or Databricks is of particular interest.
Experience working with clinical or translational datasets (e.g., clinical data models, genomics data, imaging data, or other multimodal biomedical data) particularly in oncology and/or clinical trials.
Demonstrated ability to lead independent technical or scientific initiatives that involve coordination across multiple teams, departments, or technical domains.
Experience evaluating and selecting technologies or analytical approaches in support of scientific or data infrastructure initiatives such as high performance computing tools and platforms, data-visualization applications.
Working knowledge of data governance, security, privacy, and compliance considerations associated with biomedical and clinical data.
Strong ability to translate complex scientific or operational problems into structured technical questions and potential solution pathways.
Excellent written and verbal communication skills, including the ability to produce clear technical documentation, evaluations, and recommendations for diverse audiences as well as creation of and contribution to grant proposals.
Demonstrated ability to collaborate effectively with scientists, engineers, IT partners, and institutional stakeholders.

Responsibilities

Identify technology oriented translational research pain points, integration gaps, and evaluate emerging technology needs across research, clinical, and data domains with our Translational Data Program faculty leadership.
Partner with translational researchers, faculty, and operational teams to translate scientific and operational needs into clearly defined technical problem statements.
Plan, write and execute grant-funded technology initiatives as PI, co-PI or Key Personnel on program and/or research grants for the OCDO or in support of partner grants wherein OCDO is a core or subaward.
Evaluate emerging technologies, platforms, tools, and services relevant to translational research, including cloud services, data platforms, analytics tools, and AI-enabled systems.
Assess the feasibility, interoperability, scalability, and long-term sustainability of potential technologies within our suite of tools via implementing hands-on pilot projects in partnership with our engineering leadership.
Surface technical dependencies, architectural considerations, and tradeoffs for review by Platform Architecture and Data Engineering leadership.
Identify data security, privacy, compliance, and governance considerations early in the technology evaluation process.
Partner with security, privacy, and data governance stakeholders to ensure translational use cases are appropriately assessed and aligned with institutional requirements.
Coordinate with internal technical teams and external data and/or technology vendors or partners during technology evaluation and early planning phases.
Produce clear documentation, technical assessments, and recommendations to support institutional decision-making regarding new translational technologies.
Communicate technical concepts, evaluation findings, and recommendations effectively to both technical and non-technical audiences.