Numerical Optimization Specialist

TalentBurst; Painted Post (NY); Hybrid; USD 110,000 - 150,000; Full time

Job summary

A leading company is seeking a Numerical Optimization Specialist for a hybrid role in Painted Post, NY. With a focus on large-scale optimization modeling and collaboration with cross-functional teams, the ideal candidate will hold a PhD and have significant experience in numerical optimization. The position offers the opportunity to impact manufacturing and scheduling through advanced mathematical solutions and teamwork.

Qualifications

• Minimum 10 years of experience in numerical optimization roles.
• PhD focusing on Optimization during graduate studies.
• Familiarity with GAMS or MiniZinc is optional.

Responsibilities

• Design and implement large-scale optimization models.
• Collaborate with teams to define and translate optimization problems.
• Use machine learning techniques for optimization.

Skills

Convex optimization

Large-scale optimization solvers

Python

MATLAB

Data analysis techniques

Machine learning

Education

PhD in Chemical Engineering

PhD in Computer Science

Tools

Gurobi

Pyomo

AMPL

Job description

Job Title: Numerical Optimization Specialist
Location/Type: Painted Post, NY Hybrid – remote mostly and on-site as needed
Duration: 12+ Months

Job Description:
Minimum Education: PhD in Chemical Engineering, Computer Science, or a related field with a focus on Optimization during graduate studies.

Experience: Minimum of 10 years of experience in numerical optimization roles in industry. Additional experience in academia in the field of numerical optimization is a bonus.

Travel Requirements: Domestic and international travel up to 25% to manufacturing sites.
Work Schedule: Monday-Friday 40 hours with overtime as required.
I mmigration Status: Must be eligible to work in the US for a minimum of 18 months

Key Responsibilities:
• Design, develop, and implement large-scale optimization models, including linear programming (LP), non-linear programming (NLP), and mixed-integer linear programming (MILP).
• Collaborate with supply chain specialists, planners, operations teams, and engineers to define optimization problems and translate business needs into objective functions and complex processes into mathematical constraints.
• Formulate optimization problems for model predictive control (MPC) applications and enhance existing MPC frameworks.
• Specify and gather data requirements for optimization problem formulation, working with data scientists and engineers to preprocess data.
• Develop optimization solutions using tools like Gurobi, Pyomo, AMPL, and MATLAB.
• Build optimization models from scratch and improve existing models to address challenges in manufacturing and scheduling.
• Work with business leaders to understand workflow and business logic and translate these into mathematical formulations.
• Use machine learning and data analysis techniques to define parameters, rules, and constraints for optimization problems.
• Collaborate in interdisciplinary teams and communicate optimization results effectively to technical and non-technical stakeholders.
• Document solutions and provide training to manufacturing and engineering personnel as needed.

Required Skills:
• Expertise in convex optimization, MILPs, NLPs, and large-scale optimization solvers (e.g., Gurobi).
• Proficiency in programming languages such as Python (with Pyomo), MATLAB, and AMPL. Optional: familiarity with GAMS or MiniZinc.
• Experience working with Git and version control systems.
• Strong ability to translate business processes and workflow logic into mathematical models.
• Ability to collaborate with interdisciplinary teams and communicate effectively with technical and business stakeholders.
• Strong problem-solving skills, teamwork, adaptability, and communication skills.

Desired Skills
• Familiarity with machine learning and data analysis techniques to support optimization problem formulation.
• Experience working with supply chain specialists, planners, and operations teams.
• Ability to identify and define objective functions based on business needs and workflow constraints.