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 Operations Research, Industrial Engineering, Data Science, Computer Science, Applied Mathematics, Statistics, Systems Engineering, or a related quantitative field.
• Strong experience in mathematical optimization, simulation, statistical modeling, or machine learning.
• Proficiency in programming languages such as Python, R, or similar analytical programming tools.
• Experience with optimization libraries or solvers such as Gurobi, CPLEX, OR-Tools, Pyomo, PuLP, or equivalent.
• Strong capability in data analysis, feature engineering, model validation, and algorithm design.
• Experience working with large datasets from manufacturing, supply chain, planning, or operational systems.
• Ability to structure ambiguous business problems into analytical frameworks and deliver practical solutions.
• Strong communication skills with the ability to explain complex models to both technical and non-technical stakeholders.
• Demonstrated ability to work cross-functionally in a fast-paced, global, and matrixed environment.

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.