Senior Data Scientist

Prometheus Federal Services•Fairfax, VA

4d•$100,000 - $140,000

About The Position

Prometheus Federal Services (PFS) is a trusted partner of federal health agencies. We are seeking a Senior Data Scientist to support Department of Veterans Affairs (VA) programs through advanced analytics, predictive modeling, data science, and AI-enabled solutions. This role will focus on transforming complex VA data assets into actionable insights that support operational decision-making, healthcare outcomes, data modernization initiatives, and enterprise reporting. The Senior Data Scientist will collaborate with business stakeholders, data engineers, analysts, and program leadership to design, develop, and operationalize advanced analytical solutions. The ideal candidate brings deep expertise in statistical analysis, machine learning, data engineering concepts, and healthcare analytics, along with experience working within complex federal data environments.

Requirements

Bachelor's degree in Data Science, Statistics, Computer Science, Applied Mathematics, Operations Research, Engineering, Healthcare Informatics, or a related field.
7–10 years of experience in data science, advanced analytics, machine learning, or related technical roles.
Strong proficiency in Python for data science and machine learning applications (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch, or similar).
Advanced knowledge of statistical methods, predictive modeling, and machine learning techniques.
Strong proficiency in SQL for data extraction, transformation, and analysis.
Experience developing and validating machine learning models, including classification, regression, clustering, forecasting, and anomaly detection.
Experience conducting exploratory data analysis and communicating insights to diverse audiences.
Familiarity with model evaluation techniques, performance metrics, and validation methodologies.
Experience working with large-scale structured and semi-structured datasets.
Understanding of data engineering concepts, including ETL/ELT processes, data pipelines, and cloud-based data platforms.
Strong problem-solving, critical thinking, and analytical skills.
Excellent written and verbal communication skills.
Authorized to work in the U.S. indefinitely without sponsorship.
Ability to obtain a Public Trust clearance.

Nice To Haves

Master's degree or PhD in Data Science, Statistics, Computer Science, Applied Mathematics, Operations Research, Engineering, Healthcare Informatics, or a related discipline.
Experience supporting the Department of Veterans Affairs (VA), Veterans Health Administration (VHA), or other federal healthcare agencies.
Experience working with healthcare, clinical, claims, operational, or population health datasets.
Experience with cloud-based analytics platforms such as Azure Synapse, Azure Machine Learning, Databricks, AWS, or comparable environments.
Familiarity with Power BI, Tableau, or other business intelligence and visualization platforms.
Experience supporting enterprise data modernization, governance, or digital transformation initiatives.
Experience developing explainable AI (XAI) and responsible AI solutions in regulated environments.
Experience mentoring and leading technical teams or analytical workstreams.

Responsibilities

Develop and implement advanced analytical models to identify trends, patterns, risks, and opportunities within large and complex datasets.
Design, build, and deploy machine learning models to support predictive and prescriptive decision-making.
Apply statistical techniques and data science methodologies to solve complex business and operational challenges.
Develop forecasting, classification, clustering, anomaly detection, and optimization models to support program objectives.
Support AI-enabled analytical solutions that improve operational insight, performance measurement, and resource planning.
Evaluate emerging AI, machine learning, and advanced analytics technologies for applicability within VA environments.
Develop model monitoring and evaluation frameworks to ensure accuracy, stability, explainability, and performance.
Conduct exploratory data analysis (EDA) to uncover patterns, anomalies, and key business drivers.
Perform data profiling and quality assessments to identify issues impacting analytical outcomes.
Develop feature engineering strategies to improve model performance and business relevance.
Prepare and transform structured and semi-structured datasets for advanced analytical applications.
Collaborate with data engineering teams to establish scalable analytical datasets and model-ready data pipelines.
Partner with data engineers and architects to integrate data across diverse VA systems, databases, APIs, and enterprise platforms.
Support the design and optimization of data pipelines and analytical workflows.
Contribute to data modeling efforts that improve accessibility, scalability, and performance of analytical solutions.
Assist in developing reusable analytical frameworks and data science assets across programs.
Ensure analytical solutions align with data governance, security, and compliance requirements.
Translate business questions, policy objectives, and operational needs into analytical approaches and measurable outcomes.
Present analytical findings, model outputs, and recommendations to technical and non-technical stakeholders.
Develop executive-level briefings, data visualizations, and decision-support materials.
Partner with program leadership to identify opportunities to transition from descriptive reporting to predictive and prescriptive analytics.
Communicate model assumptions, limitations, and risks to stakeholders in a clear and understandable manner.
Develop and maintain documentation for analytical methods, models, data sources, assumptions, and validation procedures.
Support analytical governance and best practices related to model lifecycle management and reproducibility.
Participate in peer reviews and quality assurance activities for analytical products.
Continuously evaluate new methodologies and technologies to improve analytical capabilities and program outcomes.
Mentor junior data scientists, analysts, and technical team members.