Data Scientist, Public Health Data Linkage (DSIS 3) DOH8990

State of Washington•Multiple Locations Statewide, WA

6d•Remote

About The Position

This home-based (remote) position is open to Washington residents and those living in Oregon and Idaho only. The Opportunity As a Data Scientist, you will work within the Linkage and Integrated Data Analysis (LIDA) Unit within the Center for Health Statistics (CHS). The LIDA Unit develops advanced data linkage methods that improve the quality, accuracy, and usability of public health data used to support research, surveillance, and decision making across Washington State. In this role, you will apply advanced statistical modeling, machine learning, and analytical judgment to solve complex data linkage problems that don't have readily available solutions. You will independently research, evaluate, and recommend approaches for integrating large, complex datasets, reasoning through tradeoffs between methods, anticipating downstream data quality and equity implications, and building the automated pipelines needed to put those solutions into production. You'll also serve as a technical expert, collaborating with epidemiologists, informaticists, data scientists, and public health partners to think through some of the Center's most challenging analytical questions. Your work will strengthen the systems used to connect vital records and other health data, providing reliable information that helps identify health trends, improve data quality, and support evidence-based public health decisions that protect and improve the health of people across Washington.

Requirements

Seven (7) years of experience developing, analyzing, or implementing advanced data science, machine learning, statistical modeling, or data engineering solutions using large and complex datasets.
A bachelor's degree in data science, computer science, statistics, biostatistics, mathematics, informatics, public health informatics, epidemiology, engineering, or another closely related technical, quantitative, or scientific field; AND Five (5) years of experience developing, analyzing, or implementing advanced data science, machine learning, statistical modeling, or data engineering solutions using large and complex datasets.
Using Python/R to develop or evaluate machine learning models, automate data processing, or analyze large and complex datasets.
Using SQL to extract, transform, integrate, and analyze large datasets.
Developing, implementing, or evaluating machine learning or data linkage models.
Developing or maintaining automated data processing or analytical pipelines.
Evaluating data quality, validating analytical outputs, and performing quality assurance.
Applying statistical analysis or modeling techniques to large, complex datasets.
Leading complex data science or analytics projects from problem definition through implementation.
Serving as a technical subject matter expert, including reviewing the work of other analysts or data scientists and advising on methodology.
Communicating technical findings and recommendations to technical and nontechnical audiences.

Nice To Haves

Experience independently selecting or designing an analytical approach when no established methodology exists, and defending that approach to technical partners.
Master’s degree or higher in informatics, data science, mathematics, computer science, statistics, biostatistics, epidemiology, social science, or related technical, quantitative or scientific field.
Experience building machine learning models using tools such as R, Python, Julia, Rust, Databricks, or similar technologies.
Experience working with large SQL-based databases.
Experience developing binary classification machine learning models, including feature engineering using raw or unstructured data.
Experience building, maintaining, or evaluating machine learning data linkage or entity resolution projects.

Responsibilities

Independently research, analyze, and evaluate complex data linkage problems that don't have readily available solutions, and recommend approaches to internal and external partners.
Design, evaluate, and improve machine learning and AI-driven data linkage models, applying statistical judgment to ensure high-quality, accurate, and equitable results.
Serve as a technical expert on data science and data linkage, reviewing and evaluating the work of other Data Science & Informatics Specialists and advising on methodology.
Test innovative and empirical methods, critically evaluate their impact on model performance, and reason through the implications of changes across the analytical pipeline.
Acquire, process, and standardize structured and unstructured data from administrative and open-source sources to strengthen data linkage performance and quality assurance.
Coach and train internal partners on the deployment of custom machine learning linkage models.
Research and evaluate emerging data science methods that improve interoperability, entity resolution, and analytical rigor.
Communicate analytical findings and technical recommendations to a variety of audiences to support data-informed public health decisions.
Contribute technical expertise during public health emergency response activities and support critical analytical needs when required.