Drive predictive analytics work for federal healthcare initiatives through end-to-end model development and operationalization.

Conduct research and development using programming, statistics, and machine learning in the federal healthcare space
Translate Medicare policy and operational problems into statistical and machine learning approaches for production model design and deployment
Build, validate, tune, and operationalize predictive models with focus on assumptions, bias, leakage, generalization, and performance
Develop production-quality Python code and machine learning pipelines that are tested, documented, version-controlled, and scalable for large data volumes
Collaborate with engineers, analysts, subject-matter experts, and government stakeholders to communicate model performance, limitations, and methodology
Support auditability and knowledge transfer across teams and stakeholders

Bachelor’s degree
10+ years of experience building and deploying data science or machine learning solutions
10+ years of experience conducting research using statistical methods, data science, and machine learning
Ability to travel 15-20%, on average, based on work you do and the clients and industries/sectors served
Must be legally authorized to work in the United States without employer sponsorship, now or at any time in the future

Master’s degree or Doctor of Philosophy degree in Statistics, Mathematics, Computer Science, Economics, Physics, Operations Research, or another quantitative field
Experience using Python and data science or machine learning libraries such as NumPy, pandas, scikit-learn, SciPy, and statsmodels
Experience with object-oriented programming, version control using Git, testing, and code review
Experience supporting healthcare or Medicare programs
Experience working with claims, enrollment, or administrative healthcare data, including:
- Centers for Medicare & Medicaid Services data
- International Classification of Diseases (ICD) codes
- Current Procedural Terminology (CPT) codes
- Healthcare Common Procedure Coding System (HCPCS) codes
Experience applying probability, statistics, linear algebra, or optimization to machine learning model development

Ability to work independently and collaborate as part of a team
Effective written and verbal communication skills
Meticulous attention to detail and quality of work product
Ability to build and sustain professional relationships
Ability to lead projects or workstreams
Ability to manage and prioritize multiple tasks in a fast-paced, dynamic environment
Strong interpersonal skills and professional demeanor
Ability to meet deadlines
Ability to provide clear guidance to others

Location: Washington, DC (onsite)

Compensation: USD 167,000 - 278,300 per yearly

Senior Data Scientist - Risk Adjustments Analytics - Associate Vice President, Data Management

Job Description