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 management and communication skills.

• Comfortable working with ambiguity and evolving business requirements.

• Passion for applying AI, optimization, and advanced analytics to real-world manufacturing challenges.

• Strong ownership mindset with the ability to drive initiatives from concept to execution.

• Collaborative style with the ability to influence across engineering, operations, planning, and leadership teams.