Senior Data Scientist Lead

We are hiring a Senior Data Scientist with deep expertise in AI agent architectures, LLMs, NLP, and hands-on development experience with A2A Protocols and Model Context Protocols (MCP). This role is integral in building interoperable, context-aware, and self-improving agents that interact across clinical, administrative, and benefits platforms. Key Responsibilities
Design and implement Agent-to-Agent (A2A) protocols enabling autonomous collaboration, negotiation, and task delegation between specialized AI agents (e.g., ClaimsAgent, EligibilityAgent, ProviderMatchAgent).
Architect and operationalize Model Context Protocol (MCP) pipelines that ensure 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 complex document understanding, intent classification, and personalized plan recommendations.
Develop retrieval-augmented generation (RAG) systems and structured context libraries to enable dynamic knowledge grounding across structured (FHIR/ICD-10) and unstructured sources (EHR notes, chat logs).
Collaborate with engineers and data architects to build scalable agentic pipelines that are secure, explainable, and 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 versioning, monitoring, and continuous model improvement. Required Qualifications
Master s 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, LangGraph, AutoGen, CrewAI, or similar multi-agent orchestration tools.
Practical knowledge and implementation experience of Model Context Protocols (MCP) for long-lived conversational memory and modular agent interactions.
Strong coding experience in Python, with proficiency in ML/NLP libraries like Hugging Face Transformers, PyTorch, LangChain, spaCy, etc.
Familiarity with healthcare benefit systems, including plan structures, claims data, and eligibility rules.
Experience with healthcare data standards like FHIR, HL7, ICD/CPT, X12 EDI formats.
Cloud-native development experience on AWS, Azure, or Google Cloud Platform including Kubernetes, Docker, and CI/CD.