Senior Data Scientist

Responsibilities

• 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.
• 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.

Qualifications

• U.S. citizen or otherwise authorized to work in the United States and able to demonstrate physical residency in the U.S. for at least three (3) of the past five (5) years.
• Must be able to obtain a U.S. Federal government client badge and pass a Public Trust clearance.
• Master’s degree in Computer Science, Data Science, or a related field required; PhD preferred.
• 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 backend systems and data integration, including data modeling and supporting APIs for web-based and production applications.
• 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.