Sr. Data Scientist

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - 12 Month(s)

Skills

LLMs
NLP

Job Details

Role: Sr. Data Scientist

Location: Woodland Hills, CA (hybrid)

Duration: 12+ Months

Must have:

  • AI agent architectures, LLMs, NLP developing A2A Protocols and Model Context Protocols (MCP)
  • LLMs and NLP models (e.g., medical BERT, BioGPT)
  • Retrieval-augmented generation (RAG)
  • Coding experience in Python, with proficiency in ML/NLP libraries
  • Healthcare data standards like FHIR, HL7, ICD/CPT, X12 EDI formats.
  • AWS, Azure, or Google Cloud Platform including Kubernetes, Docker, and CI/CD

Responsibilities:

  • Design and implement A2A protocols for autonomous task delegation and collaboration among specialized AI agents (e.g., ClaimsAgent, ProviderMatchAgent).
  • Develop MCP pipelines to enable persistent memory and context continuity across multi-turn healthcare interactions.
  • Architect and deploy LLM-orchestrated agent systems for use cases like prior authorizations, benefit optimization, and clinical summarization.
  • Fine-tune domain-specific LLMs and NLP models (e.g., medical BERT, BioGPT) for intent classification and personalized recommendations.
  • Build retrieval-augmented generation (RAG) systems with structured/unstructured healthcare data (e.g., FHIR, ICD-10, EHR).
  • Collaborate on building scalable, secure, and compliant ML pipelines (HIPAA, CMS, NCQA).
  • Lead research in memory-based agents, RLHF, and context-aware planning.
  • Contribute to end-to-end MLOps pipelines for deployment, monitoring, and iteration.

Requirements:

  • Master s/Ph.D. in CS, ML, NLP, or related field.
  • 7+ years in applied AI, particularly with LLMs, transformers, or agent systems in healthcare.
  • Proficiency with tools like LangGraph, AutoGen, CrewAI, and hands-on A2A protocol development.
  • Proven experience with Model Context Protocols, LLM pipelines, and healthcare NLP.
  • Strong Python skills with libraries such as Hugging Face, LangChain, spaCy, and PyTorch.
  • Understanding of healthcare systems (e.g., claims, eligibility, plan design).
  • Experience with healthcare data standards: FHIR, ICD/CPT, HL7, X12 EDI.
  • Cloud-native development: AWS/Google Cloud Platform/Azure, Docker/Kubernetes, CI/CD.

Preferred Qualifications:

  • Expertise in MCP + VectorDB for agent memory and dynamic context retrieval.
  • Experience building production-grade LLM agents in healthcare.
  • Background in voice AI, AI navigation, or triage systems.
  • Published work or patents in LLM-based agent systems or contextual AI.

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