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Mathematical Optimization Engineer Jobs (NOW HIRING)

Optimization Engineer - Commercial

Las Vegas, NV · On-site

$110K - $132K/yr

Summary The Optimization Engineer - Commercial transforms complex commercial decisions into model ... Bachelor's Degree in Applied Mathematics, Operations Research, Industrial Engineering, Data Science ...

Bachelor's Degree in Applied Mathematics, Operations Research, Industrial Engineering, Data Science ... optimization models using Python libraries such as PuLP, Pyomo, OR-Tools, SciPy, etc. • ...

Master's degree in Engineering, Applied Mathematics, or a related field. * Strong knowledge of optimization methods, including dynamic, parametric, and non-parametric approaches * Experience in ...

Senior MIP Developer (Global Remote)

$55.75 - $73.75/hr

A mission that focuses on mathematical optimization. We empower our customers to expand their use ... As a Senior MIP Developer at Gurobi Optimization, your primary responsibility will be to enhance ...

... optimized solutions based on Apple Silicon. In this role, you will analyze existing and new ... Minimum Qualifications Bachelor's degree in Computer Science, Computer Engineering, Mathematics ...

Showing results 41-60

Mathematical Optimization Engineer information

What does a mathematical optimization engineer do?

A Mathematical Optimization Engineer designs and implements algorithms to solve complex optimization problems in areas like logistics, finance, manufacturing, or data science. They use mathematical models and computational techniques to find the most efficient solutions to real-world challenges, such as minimizing costs or maximizing efficiency. Their work often involves collaborating with other engineers and data scientists, coding optimization routines, and translating business requirements into mathematical formulations. This role typically requires a strong background in mathematics, programming, and problem-solving.

How does a mathematical optimization engineer typically collaborate with cross-functional teams during a project?

Mathematical Optimization Engineers often work closely with data scientists, software developers, and business analysts to translate complex business problems into mathematical models. They contribute their expertise by designing, implementing, and refining optimization algorithms that fit project requirements. Regular communication is essential, as they must clearly explain technical concepts to non-experts and adapt models based on stakeholder feedback. This collaborative environment helps ensure that solutions are both technically sound and aligned with organizational goals.

What are the key skills and qualifications needed to thrive as a mathematical optimization engineer, and why are they important?

To thrive as a Mathematical Optimization Engineer, you need a strong background in mathematics, operations research, and computer science, typically supported by a relevant degree such as applied mathematics, engineering, or computer science. Familiarity with optimization solvers (like Gurobi or CPLEX), programming languages (such as Python, C++, or MATLAB), and modeling frameworks (e.g., Pyomo, AMPL) is essential, along with experience in data analysis and algorithm development. Critical thinking, problem-solving, and clear communication are crucial soft skills for collaborating effectively and translating complex mathematical concepts to practical solutions. These skills and qualities ensure robust and efficient solutions to complex optimization problems across diverse industries.

What is the difference between Mathematical Optimization Engineer vs Data Scientist?

AspectMathematical Optimization EngineerData Scientist
Required CredentialsDegree in Mathematics, Operations Research, or related fields; often certifications in optimization toolsDegree in Computer Science, Statistics, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on developing algorithms for optimization problems in industries like logistics, manufacturing, financeAnalyze large datasets to extract insights, build predictive models, and support decision-making
Employer & Industry UsageUsed in supply chain, finance, energy sectors for process improvementUsed across tech, marketing, healthcare, finance for data-driven decision making

While both roles involve analytical skills and programming, Mathematical Optimization Engineers specialize in creating algorithms to solve complex optimization problems, whereas Data Scientists focus on analyzing data to inform business decisions. The roles often overlap in industries like finance and tech but serve different core functions.

What are popular job titles related to Mathematical Optimization Engineer jobs?

For Mathematical Optimization Engineer jobs, the most frequently searched job titles are:

Infographic showing various Mathematical Optimization Engineer job openings in the United States as of September 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 86% In-person, and 14% Remote job distribution.

Optimization Engineer - Commercial

Las Vegas, NV • On-site

Allegiant
Health Care and Social Assistance • 501 - 1,000 employees

$110K - $132K/yr

Other

Posted 13 days ago


Job description

Summary

The Optimization Engineer – Commercial transforms complex commercial decisions into model-based solutions that improve profitability outcomes and automate manual processes. The position combines operations research modeling skills with software engineering fundamentals to deliver reliable, production-ready workflows. They will iterate on existing optimization logic to enhance model outputs, drive automation in production systems, debug errors in recurring runs, and collaborate with stakeholders on new feature requests. This role partners closely with the Commercial Data Science team to integrate predictive models into prescriptive processes. The employee will be expected to continuously upskill in the relevant areas and work toward ownership of their assigned systems.

Visa Sponsorship Available

No

Minimum Requirements

Combination of Education and Experience will be considered. Must be authorized to work in the US as defined by the Immigration Act of 1986. Must pass a Criminal Background Check.

Education: Bachelor’s Degree in Applied Mathematics, Operations Research, Industrial Engineering, Data Science, Statistics, or related field.

Years of Experience: Minimum two (2) years of experience in a technical environment.

  • Proficiency in writing production-quality code in Python, utilizing libraries like Pandas/NumPy and using advanced techniques such as vectorization and parallelization.
  • Strong foundation in operations research techniques, including linear/nonlinear/integer programming, network/assignment models, simulation, and/or stochastic optimization.
  • Experience building optimization models using Python libraries such as PuLP, Pyomo, OR-Tools, SciPy, etc.
  • Understanding of predictive modeling and forecasting methods such as machine learning, time series, deep learning, reinforcement learning, etc., and their implementation in Python (e.g., scikit-learn, Prophet, PyTorch, TensorFlow).
  • Knowledge of statistical concepts like regression and hypothesis testing for experiment evaluation.
  • Competency in querying data using SQL and performing exploratory analysis.
  • Ability to clearly communicate with users and stakeholders regarding feature requests, modeling choices, and experiment results through visuals, reports, and demos.
  • Demonstrated initiative, curiosity, and an ownership mindset in a fast-paced environment.
Preferred Requirements
  • Master’s degree or higher in a related field.
  • Exposure to airline economics problems such as route planning, capacity allocation, scheduling, pricing, and revenue management.
  • Experience with commercial solvers such as Gurobi, CPLEX, FICO Xpress, etc.
  • Familiarity with cloud services/model execution environments (e.g., AWS) and version control practices (e.g., GitHub).
  • Familiarity with front-end development in JavaScript and Excel VBA.
  • Fluency in the use of generative AI tools to accelerate the software development process, like GitHub Copilot and Claude Code.
Job Duties
  • Formulate airline commercial decision problems into rigorous quantitative models, applying methods from operations research, data science, statistics, and econometrics to increase revenue and decrease costs.
  • Build and improve optimization models within Python-based decision support systems by refining assumptions, constraints, objective formulations, and solution strategies to improve performance and stability.
  • Design and execute structured evaluations of model features, assumptions, and parameter changes in optimization models using back-testing, controlled experiments, or simulation to assess impact on solution quality, stability, and business outcomes.
  • Collaborate with the Commercial Data Science team to develop and incorporate machine learning solutions into relevant decision support tools.
  • Extract, transform, and load data from structured/unstructured cloud and non-cloud sources via Python and SQL to create reliable inputs for decision models and recurring workflows.
  • Support production systems end-to-end, including troubleshooting data/model issues, diagnosing performance or stability problems, and implementing improvements to robustness, monitoring, and error handling.
  • Work with stakeholders to define requirements, implement enhancements, and deliver user-facing improvements to decision support tools.
  • Communicate analytical results, model behavior, and trade-offs through clear documentation, reporting, and presentations that facilitate understanding by both technical and non-technical stakeholders/users.
  • Independently identify and execute refactors that drive automation, improve performance, reduce redundancy, and increase maintainability.
  • Contribute to a culture of continuous improvement by documenting methodologies, applying best coding practices, self-learning in relevant mathematical/computer science topics, and staying updated on airline industry trends.
  • Model Allegiant’s customer service standards in personal actions and when providing leadership direction.
  • Other duties as assigned.
Physical Requirements

The Physical Demands and Work Environment described here are a representative of those that must be met by a Team Member to successfully perform the essential functions of the role. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the role.

Office - While performing the duties of this job, the Team Member is regularly required to stand, sit, talk, hear, see, reach, stoop, kneel, and use hands and fingers to operate a computer, key board, printer, and phone. May be required to lift, push, pull, or carry up to 20 lbs. May be required to work various shifts/days in a 24-hour situation. Regular attendance is a requirement of the role. Exposure to moderate noise (i.e. business office with computers, phones, printers, and foot traffic), temperature and light fluctuations. Ability to work in a confined area as well as the ability to sit at a computer terminal for an extended period of time. Some travel may be a requirement of the role.

Essential Services Provider

Allegiant as a national air carrier is deemed an essential service provider during declared national and state emergencies. Team Members will be required to report to their assigned trip or work location during national and state emergencies unless prohibited by local, state or federal order.

EEO Statement

We welcome all individuals from varied backgrounds and experiences to apply. Our company values the unique perspectives and talents that each person brings to our team.

Equal Opportunity Employer: Disability/Veteran

For more information, see https://allegiantair.jobs

$80,000 - $100,000 a year

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