Manufacturing Data Science - IPP - Expert System

Company Overview

STMicroelectronics is a global semiconductor leader driving innovation that positively impacts people, businesses, and society. Operating in over 40 countries with a diverse workforce of more than 50,000 employees from 115 nationalities, ST combines talent with collaboration, passion, and the ambition to disrupt, innovate, and unlock potential. The company strives for a smarter, greener, and more sustainable future, powered by advanced technology.

Role Overview

As a Manufacturing Data Science expert focused on Intelligent Process Planning (IPP) and Expert Systems, you will conceptualize and create AI-driven agentic expert systems that enable predictive and autonomous decision-making within manufacturing environments.

• Design and develop agent-based and multi-agent AI systems capable of autonomous planning, decision-making, and executing actions on behalf of operators, equipment, or production lines.
• Construct reasoning frameworks and decision policies using rule-based approaches, optimization techniques, and machine learning to address complex, real-time manufacturing challenges such as dispatching, scheduling, routing, and quality control.
• Utilize simulation tools and digital twin models to replicate production flows, equipment behavior, and system constraints, enabling the safe testing and refinement of agent strategies prior to deployment.
• Incorporate predictive models—for variables like demand, machine health, cycle time, and yield—into expert systems to facilitate proactive and prescriptive decision-making.
• Work closely with process owners, planners, and equipment engineers to translate operational expertise into formalized rules, heuristics, and goals for AI agents.
• Assess the performance of agentic AI solutions using key performance indicators such as throughput, work in progress (WIP), cycle time, overall equipment effectiveness (OEE), and cost, and iteratively refine the systems accordingly.
• Document the systems’ architecture, underlying algorithms, experimental results, and support related presentations, publications, and intellectual property generation.

Candidate Profile

• Degree in computer science, AI, data science, operations research, industrial/electrical engineering, or a related field at the bachelor's or master's level.
• Robust understanding of artificial intelligence and machine learning fundamentals with practical exposure to at least one of the following domains: reinforcement learning/multi-agent reinforcement learning, planning and decision-making under uncertainty, or rule-based/knowledge-based systems.
• Proficiency in Python programming and familiarity with AI/ML ecosystems such as PyTorch, TensorFlow, scikit-learn, and reinforcement learning libraries.
• Experience or familiarity with simulation and modeling techniques relevant to manufacturing or logistics, including discrete event simulation, agent-based simulation, or digital twins.
• Knowledge of operations research and optimization problems—such as scheduling, routing, and resource allocation—is advantageous.
• Comfort and experience working with real operational and equipment datasets, including data cleaning, feature engineering, and validation.
• Strong analytical and conceptual skills, capable of translating complex shopfloor processes into formal rules, constraints, and objectives for AI agents.
• Collaborative mindset with curiosity and initiative to engage cross-functional teams and incorporate domain expertise into AI solutions.
• Proficient in business-level English, both written and spoken, to facilitate documentation, stakeholder communication, and technical presentations.

Company Commitment

STMicroelectronics is recognized as a 2025 Global Top Employer and emphasizes ethics, integrity, inclusiveness, and continuous development. The company actively promotes diversity, equity, and inclusion, encouraging applicants from various backgrounds and offering growth opportunities irrespective of the level of fulfillment of listed criteria.