Senior Data Scientist

Overview

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

Skills

AI agent architectures
LLMs
NLP
Model Context Protocols
BERT
BioGPT
FHIR
HL7
ICD/CPT
X12 EDI
Python
AWS

Job Details

Hello,

We have a job opportunity with one of our clients for a Senior Data Scientist. If interested, please respond with the details below, or appreciate it if you could refer an ex-colleague/team member/friend looking for similar roles. CardinalIT offers a referral bonus of $500 for each referral, as a gesture of gratitude and appreciation for this referral.

  • Updated Resume:
  • Expected W2/C2C:
  • Availability to start:

Looking forward to seeing your reply.

Role: Senior Data Scientist

Duration: 6+ Months Contract

Location: Woodland Hills CA (5D Onsite)

We are looking 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 Google Cloud Platform 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.

Client Job Description:

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.

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.
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