Operations Research Scientist

Corning•City of Corning, NC

12d•Hybrid

About The Position

Corning is searching for an Operations Research Scientist to join our team in Charlotte, NC or Corning NY. In this role you will design, develop, and implement mathematical optimization and simulation models to improve decision-making and operational efficiency across our Optical Communications business. Working as part of the Operations Research team, you will collaborate with data scientists, engineers, and business stakeholders to analyze complex problems in manufacturing, supply chain, planning, and commercial operations, translating data and business requirements into actionable solutions.

Requirements

Bachelor’s degree in Operations Research, Industrial Engineering, Applied Mathematics, or a related quantitative discipline.
2+ years of practical experience applying operations research 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 or R.
Strong analytical and critical thinking skills; 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.

Nice To Haves

Master’s degree preferred.
Experience with data preparation, workflow automation, and visualization is a plus.
Exposure to manufacturing, supply chain, or commercial analytics preferred.

Responsibilities

Develop and implement mathematical programming and simulation 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 and simulation tools into production using modern software and platforms.