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Principal Data Scientist

Optum

Noida

14+ years

Today

$94.0K–98.8K/yr

Full-time

Onsite

Skills Required

LLM

Gen AI

Agentic AI

RAG

LangChain

LlamaIndex

Vector Database

OpenAI

Claude

Prompt Engineering

LLM safety mechanisms

LLMOps

Agent orchestration

Evaluation frameworks

MLOps

Description

Optum is a global organization focused on improving health outcomes through technology.

The Principal Data

Scientist role involves designing and leading advanced AI solutions in healthcare and pharmacy benefit management.

Company: Optum

Role: Principal Data Scientist (SG29)

Experience

- 14+ years of overall experience in Data Science, Artificial Intelligence, Advanced Analytics, or Machine Learning
- 7+ years of hands-on experience delivering production-grade AI/ML solutions in large enterprise

Qualification

- Bachelor's degree in Computer Science
- Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, or related discipline

Responsibilities

- Lead architecture, design, and implementation of enterprise-scale AI solutions
- Define technical vision, architecture standards, reusable frameworks, and best practices for AI-driven products and platforms
- Serve as technical authority for AI solution design balancing business objectives with scalability, reliability, security, governance, and cost
- Establish reference architectures, design patterns, reusable components, and deployment frameworks
- Drive technology evaluations, proof-of-concepts, and innovation initiatives
- Design, build, and productionize Agentic AI systems with multi-step planning, reasoning, tool utilization, validation, and autonomous workflow execution
- Develop enterprise Generative AI solutions leveraging approved foundation models
- Build intelligent assistants and copilots for data exploration, pharmacy and healthcare operations, engineering productivity, knowledge management, workflow automation, clinical and business decision support
- Implement advanced RAG, Graph RAG, agent orchestration, memory management, tool-calling, and AI workflow patterns
- Design AI systems combining LLMs, knowledge graphs, search technologies, business rules, and predictive models
- Establish evaluation frameworks for LLM quality, hallucination reduction, safety, latency,



and cost optimization
- Lead development of predictive, prescriptive, and optimization models for healthcare and pharmacy business challenges
- Apply advanced statistical modeling, machine learning, deep learning, NLP, computer vision, speech technologies, and reinforcement learning
- Build hybrid AI systems integrating traditional ML models with Generative AI and Agentic AI
- Design experimentation frameworks, model evaluation methodologies, and performance optimization strategies
- Translate complex data assets into actionable insights driving measurable business improvements
- Develop AI-driven solutions using healthcare and pharmacy datasets including claims, clinical, provider, member, formulary, and utilization data
- Architect solutions improving medication adherence, clinical quality outcomes, member experience, operational efficiency, and cost management
- Work within regulated healthcare environments ensuring compliance with privacy, security, and governance
- Partner with platform, cloud, data engineering, and product teams to deploy scalable AI/ML systems
- Lead adoption of MLOps, LLMOps, and AgentOps capabilities including prompt management, model versioning, experiment tracking, automated evaluation, monitoring, observability, CI/CD integration, and governance controls
- Establish frameworks for drift detection, performance monitoring, incident response, reliability management, and cost optimization
- Build reusable libraries, APIs, accelerators, and shared AI services supporting enterprise-wide adoption
- Ensure AI solutions comply with healthcare regulatory, privacy, and security requirements
- Design and implement guardrails including prompt protection, tool access controls, data protection and redaction, PHI-protected workflows, auditability, and traceability
- Create transparent documentation covering model behavior, risks, limitations, controls, and mitigation strategies
- Promote responsible AI practices throughout solution lifecycle




- Partner with executive leadership, business stakeholders, product organizations, and engineering teams to identify high-value AI opportunities
- Influence enterprise AI strategy through technical expertise, innovation, and delivery excellence
- Communicate complex technical concepts, architectural decisions, and analytical outcomes to technical and non-technical audiences
- Mentor and guide data scientists, engineers, and AI practitioners through technical leadership and best practices
- Drive adoption through reusable artifacts, solution accelerators, reference implementations, and technical standards

Additional Responsibilities

- Comply with employment contract terms, company policies, and directives including transfer, reassignment, team changes, work shifts, flexibility of work benefits, alternative work arrangements, and other business environment changes

Nice To Have

- Master's degree or equivalent advanced experience

More Skills Classical AI, Machine Learning, Advanced Analytics, Large Language Models (LLMs), Scikit-learn, TensorFlow, PyTorch, LangGraph, OpenAI GPT/Codex, Cloud-native AI platforms, Distributed computing environments, Python, SQL, AgentOps, Retrieval-Augmented Generation (RAG), Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Speech technologies, Reinforcement learning

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