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# Data Scientist (Remote Eligible)

- **Company:** Mathematica, Inc.
- **Location:** Washington, DC, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $70,000.0 - $90,000.0
- **Contract:** Permanent contract
- **Skills:** Geographic Information Systems, Amazon Web Services, Data Analysis, Network Analysis, Clinical Data Repository, Cloud Computing, Computer Programming, Data Cleansing, R (Programming Language), Graph Database, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Markdown, Unstructured Data, Data Processing, Retrieval-Augmented Generation, Snowflake, Generative AI, Scikit Learn, Information Technology, Data Analytics, Software Version Control, Data Pipelines, Databricks
- **Published:** July 11, 2026
- **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/17555331?backUrl=%2Fcareer%2F17555331%2FData-Scientist-Remote-Eligible-D-C-Washington

## About the Role

* Master's degree in a technical field such as statistics, data science, data analytics, mathematics, operations research, computer science, and/or social science; equivalent years of experience can be substituted
 * Demonstrated interest and/or experience using data science and/or statistics to contribute to projects with a policy/social impact in academic and/or professional settings
 * Experience applying generative AI programmatically to extract insights from unstructured data, construct new features for analysis, or as a part of a larger systematic analysis
 * Experience executing causal, predictive, and descriptive data science and statistics techniques including regression modeling, machine learning algorithms, network analysis, or natural language processing
 * At least three years of experience performing data cleaning and analysis using programming languages such as R, Python, or Julia in the academic, extra-curricular, or professional environment
 * Ability and desire to work independently and take initiative as part of an interdisciplinary team that may be geographically dispersed. This includes being able to learn from resources such as academic articles, white papers, self-guided tutorials, and package documentation and willingness to constantly learn and contribute to knowledge sharing with team members
 * Experience with reproducible research principles, version control, interactive visualizations, and common packages/libraries for supporting data science work in R, Python, and/or Julia (e.g., tidyverse, data.table, R Shiny, R Markdown, pandas, polars, NumPy, scikit-learn, MLJ.jl, DataFrames.jl, and/or Makie.jl)
 * Desired but not required: experience with healthcare datasets (for example, Medicare or Medicaid claims and enrollment data), production-quality machine learning applications, cloud computing environments (AWS/Databricks/Snowflake/etc.), and algorithmic fairness and ethics

## Description

We are looking for a Data Scientist who will derive meaning from data through the creation and deployment of data-driven approaches to solve problems and answer important policy questions for clients. A Data Scientist owns data processing and analysis tasks and supports more senior level data science staff in implementing statistical, machine learning, generative AI, and other data science methods for use in research reports, internal systems, or client systems. Data Scientists will work on all aspects of the data science project life cycle, including understanding client needs, building data pipelines, monitoring data quality, developing documentation, creating visualizations, brainstorming modeling approaches, and implementing those models. Our data scientists underpin our company's core offerings in program improvement, policy assessment, and data science, which yield crucial evidence and information for policy and decision makers. This position will work remotely or flexibly in one of
our office locations.

Example projects include:

 * Build and evaluate generative AI tools to extract clinically important information from unstructured doctors' notes, then use that information to construct predictive models and descriptive statistics to improve doctor decision-making and predictive accuracy.
 * Evaluate and monitor the impacts of an alternative payment model for primary care in terms of care quality, cost, and health outcomes for diverse beneficiaries, using claims from thousands of primary care practices across the country. Use the same data to predict future hospital costs and behavior.
 * Analyze nationwide geographic access to food retailers by integrating geospatial data on retailer locations, neighborhood demographics, demand, and social vulnerability. Apply network-based accessibility analyses to compare convenient access within and across states overall and by urbanicity and retailer type and develop interactive dashboards that help policymakers identify disparities and improve access to nutrition assistance.
 * Use national survey data and grocery store purchase data to simulate realistic American diets and analyze their nutritional value. Analyze how that nutritional value compares to guidelines and what it suggests are practical, culturally aware food baskets consumers might purchase to meet the guidelines.
 * Build knowledge synthesis solutions for government and foundation clients leveraging NLP and GenAI methods (knowledge graphs, Model Context Protocol, retrieval-augmented generation) to extract quantitative information (e.g., summary statistics, regression results) and contextual details (e.g., implementation specifics, focus group discussion themes) to distill large literatures into digestible datasets that support evidence-informed policymaking.
 * Develop and evaluate a reproducible benchmarking pipeline to compare state healthcare spending against peer markets nationwide, harmonizing multi-source claims and Census data, applying statistical matching to select comparable regions, and normalizing spending through risk-adjusted regression models to support state rate-setting decisions.
 * Build and evaluate interpretable machine learning models to predict clinical care tiers from health assessment data, supporting state healthcare program's transition to a new assessment tool.
 * Partner with subject-matter experts to engineer clinically meaningful features from raw assessment items, and apply stratified sampling and diagnostics to deliver transparent models suited to high-stakes eligibility and reimbursement decisions.

Specifically, this Data Scientist contributes to team-based projects by:

 * Conducting causal, predictive, and descriptive analyses
 * Writing and maintaining programming systems in languages such as Python and R to build and evaluate models
 * Developing reliable data pipelines to obtain, combine, and transform datasets on cloud, internal, and client servers
 * Communicating technical results to diverse stakeholders including clients and cross-functional teams
 * Developing and maintaining technical and methodological documentation
 * Co-developing analysis plans with a senior data scientist or researcher
 * Leading and managing small teams and tasks with oversight from a more senior staff member


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