Senior Data Scientist, Global Operations Intelligence, SMAI at MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD. – Singapore | Jobyssey

Key Responsibilities

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.

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.

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.

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.

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.

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.