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 "Positive" 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

📌 Principal Data Scientist (Bengaluru)
🏢 Optum
📍 Bengaluru