Senior Data Scientist

Job Summary We are hiring a Senior Data Scientist with deep expertise in AI agent architectures, large language models (LLMs), and natural language processing (NLP). This role is critical in designing interoperable, context-aware, and self-improving AI agents that operate across clinical, administrative, and benefits platforms in the healthcare ecosystem. Key Responsibilities Design and implement Agent-to-Agent (A2A) protocols for autonomous collaboration, negotiation, and task delegation between specialized AI agents (e.g., ClaimsAgent, EligibilityAgent, ProviderMatchAgent). Architect and operationalize Model Context Protocol (MCP) pipelines to support persistent, memory-augmented, and contextually grounded LLM interactions across multi-turn healthcare use cases. Build intelligent multi-agent systems orchestrated by LLM-driven planning modules to streamline benefit processing, prior authorization, clinical summarization, and member engagement. Fine-tune and integrate domain-specific LLMs and NLP models (e.g., medical BERT, BioGPT) for document understanding, intent classification, and personalized plan recommendations. Develop retrieval-augmented generation (RAG) systems and structured context libraries for dynamic knowledge grounding across structured (FHIR/ICD-10) and unstructured sources (EHR notes, chat logs). Collaborate with engineers and data architects to build scalable, secure, and explainable agentic pipelines compliant with healthcare regulations (HIPAA, CMS, NCQA). Lead research and prototyping in memory-based agent systems, reinforcement learning with human feedback (RLHF), and context-aware task planning. Contribute to production deployment through robust MLOps pipelines for model versioning, monitoring, and continuous improvement. Required Qualifications Masters or Ph.D. in Computer Science, Machine Learning, Computational Linguistics, or a related field. 7+ years of experience in applied AI with a focus on LLMs, transformers, agent frameworks, or NLP in healthcare. Hands-on experience with Agent-to-Agent protocols and multi-agent orchestration tools (e.g., LangGraph, AutoGen, CrewAI). Practical experience implementing Model Context Protocols (MCP) for long-lived conversational memory and modular agent interactions. Strong programming skills in Python and proficiency with ML/NLP libraries (e.g., Hugging Face Transformers, PyTorch, LangChain, spaCy). Familiarity with healthcare benefit systems, including plan structures, claims data, and eligibility rules. Experience with healthcare data standards such as FHIR, HL7, ICD/CPT, and X12 EDI formats. Cloud-native development experience on AWS, Azure, or Google Cloud Platform, including Kubernetes, Docker, and CI/CD. Preferred Qualifications Deep understanding of MCP + VectorDB integration for dynamic agent memory and retrieval. Prior experience deploying LL1M-based agents in production healthcare systems. Experience with voice AI, automated care navigation, or AI triage tools. Published research or patents in agent systems, LLM architectures, or contextual AI frameworks. Education: Bachelors Degree, Doctoral Degree