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