Senior Data Scientist, Global Operations Intelligence, SMAI

Key Responsibilities Capacity Optimization & Advanced Analytics • Develop optimization models to improve factory capacity utilization, throughput, cycle time, tool loading, and bottleneck management. • Build mathematical models for capacity planning, production allocation, constraint identification, and investment prioritization. • Apply operations research techniques such as linear programming, mixed-integer programming, constraint programming, stochastic optimization, and simulation-based optimization. • Design algorithms to support factory maxout strategies and identify opportunities to unlock additional capacity without unnecessary capital investment. Semiconductor Manufacturing Problem Solving • Partner with manufacturing, industrial engineering, planning, equipment, process, and business teams to understand capacity constraints and operational challenges. • Analyze tool capability, process flows, product mix, WIP movement, cycle time, dispatching rules, and factory constraints to recommend optimization opportunities. • Support scenario analysis for capacity expansion, product mix changes, technology transitions, and capital planning decisions. • Develop data-driven recommendations that improve decision quality across tactical and strategic planning horizons. Data Science, AI/ML & Decision Intelligence • Build predictive and prescriptive analytics models using large-scale manufacturing and planning datasets. • Apply machine learning techniques to forecast capacity demand, identify abnormal patterns, predict bottlenecks, and recommend operational actions. • Integrate optimization engines with data pipelines, visualization dashboards, and decision-support tools. • Collaborate with software engineering teams to deploy scalable analytical models into production systems. Stakeholder Engagement & Business Impact • Translate complex analytical findings into clear, actionable insights for technical teams and business leaders. • Quantify business impact in terms of capacity gain, cost avoidance, cycle time reduction, productivity improvement, and capital efficiency. • Drive cross-functional alignment by communicating assumptions, model logic, trade-offs, and recommendations effectively. • Contribute to roadmap development for advanced capacity intelligence, factory digital twin, and AI-driven planning capabilities. Required Qualifications • Master's degree or higher in a quantitative field such as Electrical Engineering, Statistics, Mathematics, or a related discipline. • Strong experience in mathematical optimization, statistical modeling, machine learning, reinforcement learning, and algorithm development. • Strong programming skills in Python, R, and MATLAB for data analysis, modeling, simulation, and machine learning applications. • Experience developing optimization algorithms, including heuristic-based optimization, resource allocation under constraints, dynamic programming, and sequential decision-making models. • Experience designing and evaluating reinforcement learning (RL) models, Markov Decision Processes (MDPs), and learning-based decision systems. • Experience building simulation pipelines and conducting large-scale numerical experiments to evaluate algorithm performance and decision quality. • Strong capability in data analysis, statistical inference, model validation, predictive modeling, and performance benchmarking. • Experience processing and analyzing large-scale datasets and applying machine learning techniques to generate actionable insights. • Familiarity with deep learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI concepts. • Strong written and verbal communication skills, demonstrated through peer-reviewed publications and the ability to translate analytical results into actionable insights for stakeholders. Preferred Qualifications • PhD in Operations Research, Industrial Engineering, Applied Mathematics, Systems Engineering, or a closely related field. • Strong semiconductor manufacturing experience, particularly in wafer fabrication, assembly/test, advanced packaging, capacity planning, or industrial engineering. • Deep understanding of semiconductor manufacturing concepts such as process flows, tool groups, WIP, cycle time, bottlenecks, dispatching, product mix, yield, and equipment utilization. • Experience developing capacity planning, production scheduling, factory simulation, or digital twin solutions. • Hands-on experience with discrete-event simulation, agent-based simulation, or factory simulation platforms. • Experience deploying optimization or AI/ML models into production environments. • Familiarity with manufacturing systems such as MES, ERP, APS, data warehouses, or planning platforms. • Knowledge of cloud platforms, data engineering pipelines, APIs, and scalable model deployment is a plus. • Experience leading analytical projects from problem definition through implementation and business adoption. • Proven track record of delivering measurable business impact through optimization, automation, or AI-driven decision support. Key Technical Skills • Mathematical optimization: LP, MILP, nonlinear optimization, constraint programming, stochastic optimization. • Simulation: discrete-event simulation, what-if analysis, scenario modeling, digital twin concepts. • Data science: regression, classification, clustering, time-series forecasting, anomaly detection, predictive modeling. • Programming: Python, SQL, R, Spark, Git. • Optimization tools: Gurobi, CPLEX, OR-Tools, Pyomo, PuLP. • Visualization and communication: Power BI, Tableau, Plotly, Dash, or equivalent. • Manufacturing analytics: capacity modeling, bottleneck analysis, tool utilization, cycle time, WIP flow, throughput modeling. Core Competencies • Strong analytical and structured problem-solving mindset. • Ability to balance technical rigor with practical business implementation. • Excellent stakeholder m…