Staff Data Scientist, SMAI OI

We are seeking a highly motivated Agentic AI Data Scientist to lead the design and deployment of next-generation AI agents that drive end-to-end planning and operational optimization within SMAI (Smart Manufacturing & AI).

This role focuses on building goal-driven, multi-step AI systems (agents) that can autonomously plan, decide, and execute workflows across manufacturing planning, capacity optimization, and operations intelligence-unlocking cycle time reduction, capacity improvements, and decision automation at scale.

Key Responsibilities

1. Agentic AI Design & Development

• Design and develop agent-based AI systems capable of:

• Multi-step reasoning (planning + decision-making + execution)

• Autonomous orchestration across workflows and platforms

• Build multi-agent architectures for complex planning and operations use cases

• Develop agents that integrate:

• Optimization models (OR / mathematical programming)

• LLM-based reasoning and tool usage

• Ensure agents align with enterprise data, domain knowledge, and planning constraints

2. Planning & Operations Use Case Delivery

• Apply Agentic AI to key business problems such as:

• Capacity planning and capital investment optimization

• Production flow optimization and cycle time reduction

• Scenario simulation and decision support

• Translate business requirements into:

• Structured optimization problems

• AI-driven decision workflows

3. AI System Integration & Deployment

• Integrate agents into:

• Existing SMAI platforms and tools

• Data pipelines and enterprise systems

• Develop reusable frameworks for:

• Agent orchestration

• Knowledge retrieval (RAG / knowledge graph)

• Drive deployment strategy (embedded vs standalone agents depending on use case)

4. Cross-Functional Collaboration

• Partner with:

• Planning, Operations, and Manufacturing teams

• Data Engineering, MLOps, and Platform teams

• Translate domain knowledge into AI logic and workflows

• Communicate technical solutions to business stakeholders

Required Skillsets

Core AI / Software Engineering

• Strong programming skills in Python (preferred), plus familiarity with modern AI frameworks

• Experience with LLMs / GenAI ecosystems (e.g., agent frameworks, tool-use, orchestration)

• Solid understanding of:

• Prompt engineering

• Retrieval-Augmented Generation (RAG)

• Multi-agent systems

Optimization & Decision Science (Critical)

• Strong background in Operations Research / Optimization, including:

• Linear / Mixed Integer Programming

• Heuristics / metaheuristics

• Simulation models

• Experience translating real-world planning problems into mathematical models

Agentic AI & System Design

• Understanding of agentic AI principles:

• Goal-based and utility-based agents

• Planning + reasoning + execution loops

• Experience designing:

• Autonomous workflows

• Multi-step decision systems

• Tool-using AI agents

Data & Systems Integration

• Experience working with:

• Structured and unstructured data

• APIs and enterprise systems integration

• Familiarity with:

• Data pipelines (e.g., Spark, SQL)

• MLOps / deployment pipelines

Business & Domain Skills (Preferred)

• Experience in manufacturing, supply chain, or planning domains

• Strong problem-solving skills with ability to:

• Connect AI solutions to business value

• Quantify impact (capacity, cost, cycle time)

Minimum Qualifications

• Bachelor's or Master's degree in:

• Computer Science, Data Science, Industrial Engineering, Operations Research, or related field

• 3-5+ years of experience in:

• AI/ML engineering, or

• Optimization / decision science, or

• Advanced analytics in operations/planning

• Proven experience building production-grade AI or optimization solutions

Preferred Qualifications

• PhD in AI, Machine Learning, or Operations Research

• Experience with:

• Agent frameworks (LangChain, AutoGen, CrewAI, etc.)

• Reinforcement learning or adaptive systems

• Knowledge graphs and domain-specific AI tuning

• Experience in semiconductor or advanced manufacturing environments