Statistician (Data Scientist)

Goldbelt, Inc.•District of Columbia, DC

2d•Onsite

About The Position

The Statistician (Data Scientist) will apply advanced statistical and machine learning techniques to analyze complex healthcare datasets, uncover patterns, and generate predictive insights. The role supports healthcare analytics, including predictive modeling, quantitative evaluation of clinical and operational initiatives, data pipeline development, data visualization, and the communication of analytical findings to senior leaders and other stakeholders. The primary work location is within the National Capital Region (VA/MD/DC), with duties performed primarily on-site at assigned DHA/MTF locations. Situational telework may be available, subject to management approval, operational needs, and policy compliance. This position is contingent upon the successful award of a contract currently under bid.

Requirements

Bachelor's degree in Healthcare Informatics, Statistics, Mathematics, Computer Science, Public Health, or a related quantitative field.
At least 5 years of MHS or VA healthcare data analytics experience.
Experience with healthcare data and tools such as SQL, Python, and R.
Active Secret security clearance.
Strong organizational skills to manage multiple documents and deadlines effectively.

Nice To Haves

Additional related certifications.

Responsibilities

Develop and validate predictive models to forecast patient demand, resource utilization, disease progression, and other healthcare outcomes.
Conduct quantitative analyses to evaluate clinical and operational initiatives.
Design, build, and maintain scalable data extraction, transformation, and loading pipelines.
Serve as a technical expert on MHS data systems and ensure data integrity.
Develop interactive data visualizations, dashboards, and reports using tools such as Tableau or Power BI.
Translate analytical findings into actionable recommendations and strategic insights for senior leaders.
Collaborate with stakeholders to understand analytical needs and develop custom data solutions.
Perform routine data quality assessments and data cleansing.
Document data sources, methodologies, and analytical processes for reproducibility and transparency.
Present analytical findings clearly to technical and non-technical audiences.
Provide data and analytical support for ad-hoc requests and special projects.
Stay current with trends and technologies in data science and healthcare analytics.