The posting
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.
