Senior Data Scientist and AI Specialist
- Design, build, and deploy production-grade AI/ML systems across NLP, Retrieval-Augmented Generation (RAG), forecasting, and advanced analytics use cases.
- Fine-tune and operationalize large language models (LLMs) and RAG pipelines, including embedding strategy, vector store design, retrieval evaluation, and prompt/grounding patterns that meet the accuracy, traceability, and data-handling standards required in regulated health & human services environments.
- Establish and maintain end-to-end MLOps on Azure (e.g., Azure ML, pipelines, model registry, CI/CD, monitoring, and drift detection), ensuring models are reproducible, secure, observable, and reliably maintained in production rather than one-off prototypes.
- Develop SQL data models and analytics solutions that integrate and structure data from child welfare and Medicaid program systems, translating fragmented agency data into trustworthy inputs for AI/ML systems and operational reporting that improve efficiency and surface actionable insight.
- Partner with cross-functional teams, product owners, agency stakeholders, engineers, and policy/program staff - to translate public sector business problems into technical solutions, and to clearly communicate model behavior, limitations, and outcomes to both technical and non-technical audiences.
- 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 and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
- Bachelor's degree required
- 5+ years experience with Python
- 5+ years experience with SQL
- 5+ years experience with Microsoft Azure
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
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