Senior Data Scientist

The Senior Data Scientist applies advanced analytics, statistical modeling, machine learning, and emerging artificial intelligence technologies to address complex healthcare and policy challenges. This role combines deep technical expertise with healthcare domain knowledge to transform complex data into actionable insights, support evidence-based decision-making, and drive innovation across Medicaid and CHIP programs.

The incumbent will lead the development of analytical solutions, evaluates and implements advanced technologies including NLP and LLM/RAG frameworks, and collaborates with stakeholders to design scalable, data-driven products that improve program oversight, operational performance, and health outcomes.

• Serve as a technical lead on AI and machine learning initiatives, providing guidance on solution architecture, model selection, implementation approaches, and technical best practices.   

• Mentor and support junior and mid-level data scientists through code reviews, knowledge sharing, technical coaching, and collaborative problem solving.   

• Establish and promote best practices for MLOps, model evaluation, model monitoring, reproducibility, and responsible AI development   

• Build and deploy end-to-end ML pipelines on AWS (e.g., SageMaker, S3, Glue) for scalable training, evaluation, and inference.  

• Develop and implement advanced NLP solutions, including text classification, entity recognition, topic modeling, and semantic search using models such as BERT and transformer-based architectures.  

• Design, build, and productionize RAG (Retrieval-Augmented Generation) systems, including document ingestion, embedding pipelines, vector search, and LLM orchestration.  

• Design and implement a scalable knowledge graph and semantic data model that captures relationships among policies, analytic use cases, data domains, information assets, products, and institutional knowledge, enabling advanced search, discovery, impact analysis, and AI-assisted knowledge retrieval. 

• Develop LLM-powered applications, including prompt engineering, evaluation frameworks, and optimization techniques for accuracy, consistency, and cost.  

• Contribute to agentic AI system design, including multi-step reasoning workflows, tool use, and orchestration of LLM-driven agents for complex tasks.  

• Implement predictive analytics and statistical modeling to uncover patterns, trends, and insights from healthcare data.  

• Evaluate emerging AI technologies, frameworks, and techniques and recommend their appropriate application to government healthcare use cases.  

• Perform data mining and exploratory data analysis (EDA) using state-of-the-art techniques across structured and unstructured datasets.  

• Contribute to technical leadership across multiple AI initiatives while remaining an active hands-on developer and model builder.  

• Build data visualizations, dashboards, and analytical tools to communicate findings clearly to technical and non-technical stakeholders.  

• Evaluate model performance using appropriate metrics (e.g., accuracy, AUC, precision/recall) and present results in a clear, actionable manner.  

• Collaborate in an Agile environment with cross-functional teams including engineers, analysts, and stakeholders.  

• Recommend data-driven solutions and AI strategies aligned with CMS business needs and healthcare policy objectives.  

• Master’s degree in Computer Science, Data Science, or a related field required; PhD preferred.  A minimum of ten (10) years of experience or an equivalent combination of education and experience, with five (5) or more years of experience as a Data Scientist or in a similar role.

• Strong experience in machine learning and statistical modeling, including supervised and unsupervised learning techniques, deep learning, and a solid foundation in probability, hypothesis testing, and regression.  

• Demonstrated experience serving as a technical lead, senior individual contributor, or subject matter expert on machine learning or AI projects.  

• Proven track record of deploying, maintaining, and monitoring machine learning and AI solutions in production environments.  

• Strong understanding of MLOps practices, including model versioning, CI/CD workflows, monitoring, testing, and operational support.  

• Proven expertise in NLP and text analytics, including transformer-based architecture (e.g., BERT and related models), embeddings, vector databases, and semantic search systems.  

• Hands-on experience building LLM-powered applications, including prompt engineering, RAG architecture, and ideally agentic workflows or LLM orchestration frameworks, preferably within AWS environments (e.g., Bedrock).  

• Advanced programming skills in Python (preferred) and/or R, with practical experience using ML and data libraries such as pandas, NumPy, scikit-learn, PyTorch, and TensorFlow.  

• Strong experience with AWS cloud and MLOps tooling, including SageMaker, S3, Glue, Airflow, and data stores such as Redshift and DynamoDB, along with version control (GitHub) and CI/CD pipelines (e.g., Jenkins).  

• Experience with developing and using knowledge graphs strongly preferred.  

• Experience with backend systems and data integration, including data modeling and supporting APIs for web-based and production applications.  

• Experience working with large healthcare datasets, especially Medicaid, a plus 

• Strong written and verbal communication skills, with the ability to explain complex models and insights clearly.  

• Experience supporting CMS or other federal healthcare agencies is a plus.