Develop and implement mathematical programming, simulation models, and cost models (e.g., linear and mixed-integer programming, discrete-event simulation) to address real-world business challenges.
Analyze process flows, resource allocation, production planning, and logistics scenarios, recommending evidence-based improvements.
Collect, clean, and analyze operational data; translate findings into model parameters and actionable insights.
Collaborate with cross-functional teams to frame business problems, gather requirements, and present results to technical and non-technical audiences.
Document models, methodologies, and results; support knowledge sharing and team best practices.
Assist in deploying optimization, simulation, and cost modeling tools into production using modern software and platforms.
Bachelor s degree in Operations Research, Industrial Engineering, Applied Mathematics, or a related quantitative discipline.
7+ years of practical experience applying operations research and industrial engineering techniques to solve business or engineering problems.
Proficiency with optimization solvers (e.g., CPLEX, Gurobi, Pyomo + GLPK), simulation tools (e.g., AnyLogic, SimPy), and programming languages such as Python.
Strong analytical and critical thinking skills with attention to detail and a passion for problem-solving.
Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.
Collaborative team player with strong interpersonal skills.
Curious, adaptable, and eager to continuously learn and apply new methods.
Results-oriented and proactive in driving projects to completion.
Master s degree preferred.
Experience with data preparation, workflow automation, and visualization is a plus.
Exposure to manufacturing, supply chain, or commercial analytics preferred.