Staff Data Scientist, SMAI OI
Job Description
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