Data Scientist II

Fred Hutchinson Cancer Center•Seattle, WA

11d

About The Position

The Data Scientist II will support clinical and translational data science at the Fred Hutch. The data scientist will use multimodal, real-world healthcare data (electronic health records, registry data, etc.) as well as multimodal data sets generated in clinical and translational labs such as genomics and imaging, to advance our understanding of clinical care and patient outcomes. The data scientist will work on collaborative projects utilizing our OMOP common data model for observational oncology data in combination with diverse multimodal datasets. Projects may include developing predictive clinical models, LLM/AI integrated analyses, statistical modeling to understand patient populations and cancer outcomes, and cohort identification. The data scientist will develop templates and workflows for the data science community at Fred Hutch and play a key role in building ethical data stewardship and management skills across Fred Hutch. 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.

Requirements

B.A., B.S. in computational biology, biostatistics, computer science, data science, biophysics, bioinformatics, or a related field.
Minimum 5 years of experience in data science, bioinformatics, or related disciplines.
Graduate degrees can apply towards this minimum (M.A./M.S. = 2 years, PhD = 4+ years)

Nice To Haves

Graduate degree in epidemiology, biostatistics, statistics/mathematics, public health, computational biology, bioinformatics, biology, or a related field.
Proficiency in R and/or Python programming.
Strong SQL and experience working in cloud-based data ecosystems.
Experience working with the OMOP common data model and OHDSI tools.
Demonstrated knowledge of machine learning and deep learning.
Demonstrated rigor and reproducibility through well organized and well documented code and/or committed to a public code repository.
Experience using Git in a collaborative setting.
Experience working with standardized medical vocabulary and ontologies (ICD-10, SNOMED, RxNorm, etc.)
Experience with data governance processes for working with regulated data (HIPAA, GDPR, etc.).
Strong oral and written communication skills, and the ability to prioritize written documentation.
Excellent interpersonal and communication skills with audiences with a wide range of data expertise.
A functional understanding of medical oncology, cancer epidemiology, or immunotherapy.
Proficiency in natural language processing tasks and tools, especially with clinical text.

Responsibilities

Participates in collaborative data science projects with academic researchers, oncologists, statisticians, and other partners.
Develops reproducible statistical analyses and machine learning models for topics such as: cohort identification, clinical characterization, and patient-level prediction.
Thinks critically and communicates clearly throughout the data science lifecycle (problem formulation, data cleaning, EDA, analysis, evaluation, and communication of results).
Creates data visualizations, reports, queries, and summaries to communicate findings to collaborators.
Supports development and growth of our common data model (OMOP) for real-world, observational health research through investigating data quality, conducting exploratory analysis on new data sources, and making recommendations for data transformation requirements.
Identifies, understands and creates multimodal data packages integrating clinical and research laboratory data to create comprehensive descriptions of patient journeys.
Collaborates with the translational data science team to develop templates, packages, and documentation for the data science community at Fred Hutch and self-serve analytics users.
Ensures analyses meet a high standard for methodological rigor and stays current in methodology for real-world evidence studies.
Complies with data governance and data privacy policies.