Principal Data Scientist

Job Summary

As a Senior Data Scientist (SG 29), you will hands on design, build, and productionize Agentic AI and GenAI solutions on top of a Healthcare Core Data Platform. You will deliver reliable, compliant, and scalable AI systems that work across large healthcare datasets (claims/clinical/provider/member) and enable measurable improvements in quality, cost, and operational efficiency.

Primary Responsibilities

• Agentic AI & GenAI Delivery
  • Design and implement agentic AI systems (multi step, tool using agents) that can plan, execute, and verify outcomes under defined guardrails for healthcare workflows
  • Build GenAI solutions using enterprise approved LLMs including Claude 4.6 and OpenAI Codex (and equivalents) for:
    • intelligent data exploration and analytics assistance
    • automated insight generation and summarization
    • engineering productivity accelerators (code generation, refactoring, test creation)
    • workflow automation and triage support
  • Develop hybrid systems combining LLMs with classical ML (predictive/prescriptive models) for robust performance on healthcare use cases
• Core Data Platform Integration
  • Implement AI solutions tightly integrated with the Core Data Platform (curated datasets, standardized semantics, governed access, reusable components)
  • Partner with data engineering/platform teams to implement scalable patterns for:
    • secure tool-calling and controlled data access
    • retrieval / grounding patterns (enterprise search, knowledge bases, curated datasets)
    • reusable agent skills and shared libraries
• Model / Agent Lifecycle Ownership
  • Own end to end delivery: problem framing - data readiness - prototyping - evaluation - production deployment - monitoring and iteration
  • Build and maintain evaluation frameworks for LLM and agent behavior (quality, hallucination risk, safety, latency, and cost)
  • Implement drift and behavior monitoring for models and agents; create feedback loops and runbooks to maintain performance over time
  • Ensure production readiness: reliability, observability, incident response, and cost controls
• Governance, Security & Responsible AI
  • Build solutions compliant with healthcare privacy and governance expectations (e.g., PHI handling, access controls, auditability, retention, and policy adherence)
  • Implement guardrails: prompt protections, tool use restrictions, sensitive data redaction, and explainability approaches where required
  • Document system behavior, limitations, and risk mitigations for technical and non technical stakeholders
• Technical Influence
  • Influence adoption through hands-on artifacts: reference implementations, templates, evaluation harnesses, reusable agent patterns, and technical documentation
  • Contribute to platform standards for LLMOps / AgentOps: telemetry, gating checks, prompt/version management, and secure deployment patterns

Required Qualifications:

• Bachelor''s or Master''s degree in Computer Science, Data Science, Statistics, Engineering, or related field
• 12+ years of overall experience, with 5+ years in Data Science / AI/ML / Applied AI in enterprise environments
• Hands-on experience with ML frameworks: Scikit Learn, and at least one of TensorFlow / PyTorch
• Experience building evaluation + monitoring for ML/AI systems (metrics, drift, observability, reliability)
• Practical experience applying LLMs and coding assistants in real workflows, including Claude 4.6 and OpenAI Codex (or equivalent enterprise-approved tools/models)
• Solid proficiency in Python (pandas, numpy) and SQL; solid debugging and problem-solving skills
• Proven track record delivering production-grade AI/ML solutions end to end (not just experimentation)

Preferred Qualifications:

• Hands-on experience with agentic orchestration patterns: tool calling, memory strategies, guardrails, and multi-step workflow design
• Experience with streaming/event-driven systems (Kafka) for near-real-time use cases
• Exposure to LLMOps / AgentOps: prompt/version management, automated evaluation, red-teaming, telemetry, CI/CD integration
• Familiarity with big data / distributed compute (PySpark, distributed SQL engines) and large-scale pipelines
• Healthcare domain familiarity: claims/clinical/provider/member data and regulated delivery environments (privacy/security/compliance)
• Proven ability to own complex problem spaces end-to-end with minimal supervision, from design through production operations
• Proven ability to drive impact primarily through technical execution and reusable artifacts, not people management
• Proven ability to make engineering tradeoffs across accuracy, safety, latency, reliability, compliance, and cost and documents decisions clearly
• Proven ability to produce solutions that are adoptable and repeatable across multiple assets via templates, libraries, and reference implementations

Success Measures (What "Good" Looks Like in 6-12 Months)

• Production Adoption: agent/GenAI capability used by multiple downstream teams or workflows
• Quality & Safety: measurable improvements in evaluation scores; reduced hallucination/safety incidents via guardrails
• Operational Excellence: monitoring coverage, drift detection, and incident response readiness implemented
• Productivity: reduced cycle time for analytics/engineering workflows via Claude 4.6/Codex-enabled accelerators