Multi-Disciplined Scientist (Painted Post)
JOB DESCRIPTIONTitle: Multi-Disciplined Scientist IIILocation: Painted Post, NY, USA 14870Type: 100% On-Site (W2 Contract)Interview Process: 2 Rounds via Teams (Hiring Manager + Team Panel)Relocation: Open to non-local candidates willing to relocate at their own expenseKey ResponsibilitiesDevelop and implement mathematical programming, simulation, and cost models including: Linear Programming, Mixed-Integer Programming, Discrete-Event SimulationAnalyze process flows, production planning, logistics scenarios, and resource allocation to recommend operational improvements.Collect, clean, validate, and analyze operational data to build model parameters and actionable business insights.Collaborate with cross-functional teams to define business problems, gather requirements, and deliver analytical solutions.Present technical findings and recommendations to both technical and non-technical stakeholders.Document methodologies, models, assumptions, and results to support knowledge sharing and best practices.Assist in deploying optimization, simulation, and cost modeling tools into production environments using modern software platforms.Required QualificationsBachelor's degree in: Operations Research, Industrial Engineering, Applied Mathematics Or related quantitative discipline7+ years of hands-on experience applying operations research and industrial engineering techniques in business or engineering environments.Strong experience with optimization solvers such as:CPLEX,Gurobi Pyomo,GLPKExperience with simulation tools such as: AnyLogic, SimPyStrong programming experience in Python.Excellent analytical, critical thinking, and problem-solving abilities.Strong communication skills with the ability to explain complex concepts clearly.Collaborative team player with strong interpersonal skills.Self-driven, adaptable, and proactive mindset.Preferred QualificationsMaster's degree preferred.Experience with: Data preparation, Workflow automation, Data visualizationExposure to: Manufacturing, Supply Chain, Commercial analytics