Simulation Engineer

Key Responsibilities:

• Model Development: Develop complex simulation models using SimPy, AnyLogic, or FlexSim to represent real-world operational processes (e.g., warehousing, logistics networks, manufacturing lines) per the design and specifications from Google SMEs
• Scenario Analysis: Execute comprehensive "what-if" analyses to test changes in layout, staffing, logic, or equipment before physical implementation as per the provided specifications.
• Data Integration: Collaborate with SME’s to clean and prepare input data, defining statistical distributions (Poisson, Gaussian, etc.) that accurately reflect process variability.
• Optimization: Apply Operations Research techniques (mixed integer programming, heuristics, scheduling, graph optimization and queueing theory) within simulation environments to identify global optima versus local improvements.
• Verification: Conduct rigorous testing to ensure model behavior matches historical data and reality.

Required Qualifications

• Educational Background: BS or MS in Operations Research, Industrial Engineering, Systems Engineering, Computer Science, or a related quantitative field.

• Simulation Expertise:

• Deep understanding of Discrete Event Simulation (DES) principles (entities, resources, queues, events).

• Proven experience with Agent-Based Simulation (ABS) for modeling decentralized, autonomous behaviors.

• Technical Stack:

• SimPy: Strong proficiency in Python-based simulation modeling; ability to write custom classes and generators.

• Commercial Tools: Hands-on experience with AnyLogic (Java-based scripting) or FlexSim (FlexScript/SQL).

• Operations Research: Solid foundation in queueing theory, stochastic processes, and mathematical optimization.

• Programming: Integration of version control, automated testing/verification and code review practice. Proficiency in Python (pandas, NumPy, SciPy), Java for model building, data engineering and analysis.

Preferred Qualifications

• Advanced querying with SQL for data extraction.
• Demonstrated ability to solve complex problems through custom algorithm development.
• Strong aptitude for breaking down advanced technical requirements into efficient code in Python, C++, Java, etc.,
• Knowledge of Java (specifically for advanced AnyLogic customization).
• Experience integrating simulation models with AI/Reinforcement Learning agents.
• Familiarity with visualization tools like Tableau, PowerBI, or Streamlit.