Senior Data Scientist

Hello;

Location is Woodland Hills, CA /Mason OH

Mandatory Areas

Must Have Skills

We are looing for Senior Data Scientist with 15+ Years

Skill 1 7+ Yers Exp - AI agent architectures, LLMs, NLP developing A2A Protocols and Model Context Protocols (MCP)

Skill 2 - 7+ Yers Exp - LLMs and NLP models (e.g., medical BERT, BioGPT)

SKill 3 - 7+ Yers Exp - retrieval-augmented generation (RAG)

Skill 4 7+ Yers Exp - coding experience in Python, with proficiency in ML/NLP libraries

Skill 5 - 7+ Yers Exp - healthcare data standards like FHIR, HL7, ICD/CPT, X12 EDI formats.

Skill 6 - 7+ Yers Exp - AWS, Azure, or GCP including Kubernetes, Docker, and CI/CD

Preferred Qualifications

Deep understanding of MCP + VectorDB integration for dynamic agent memory and retrieval.

Prior work on LLM-based agents in production systems or large-scale healthcare operations.

Experience with voice AI, automated care navigation, or AI triage tools.

Published research or patents in agent systems, LLM architectures, or contextual AI frameworks.

Domain Experience (If any ) Good to have healthcare experience

Must have Certifications None

Prior UST experience Preferably

If Yes provide dates , details of account/project

Location - WOODLAND HILLS

Onsite Requirement - onsite need technically strong candidates

Number of days onsite - M-F

JD:

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 GCP including Kubernetes, Docker, and CI/CD.

Preferred Qualifications

Deep understanding of MCP + VectorDB integration for dynamic agent memory and retrieval.

Prior work on LLM-based agents in production systems or large-scale healthcare operations.

Experience with voice AI, automated care navigation, or AI triage tools.

Published research or patents in agent systems, LLM architectures, or contextual AI frameworks.