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Mathematical Optimization Jobs (NOW HIRING)

We are looking for a Software Engineer with deep expertise in Mathematical Optimization and quantum algorithm development. This role is critical in architecting the core software engine that drives ...

We are looking for a Software Engineer with deep expertise in Mathematical Optimization and quantum algorithm development. This role is critical in architecting the core software engine that drives ...

Develop and improve control and optimization algorithms * Design, write, test, and deploy ... Strong mathematical skills, with the ability to apply new mathematical techniques to real-world ...

Develop and improve control and optimization algorithms * Design, write, test, and deploy ... Strong mathematical skills, with the ability to apply new mathematical techniques to real-world ...

This role develops mathematical optimization, machine learning, and AI solutions, and contributes to the data pipelines that feed them - to support cost reduction, manufacturing efficiency, and ...

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Mathematical Optimization information

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$102K

How much do mathematical optimization jobs pay per year?

As of Sep 7, 2026, the average yearly pay for mathematical optimization in the United States is $55,794.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,000.00 and $72,500.00 per year, depending on experience, location, and employer.

What is a mathematical optimization?

A Mathematical Optimization job involves using mathematical techniques and algorithms to find the best possible solution to a given problem while satisfying constraints. Professionals in this field work in industries like finance, logistics, engineering, and artificial intelligence to optimize processes, minimize costs, or maximize efficiency. They use tools like linear programming, integer programming, and machine learning to solve complex decision-making problems.

What do mathematical optimization professionals do?

Professionals in Mathematical Optimization often work on projects involving resource allocation, supply chain management, scheduling, logistics, network design, or financial portfolio optimization. They use mathematical models to define and solve problems where the objective is to maximize efficiency or minimize costs under various constraints. Work may include collaborating with cross-functional teams to gather requirements, analyze large datasets, develop optimization algorithms, and implement solutions within existing business systems. These roles are found across industries such as manufacturing, transportation, finance, and technology, providing diverse and challenging opportunities. This variety in project scope allows for continuous learning and professional growth.

What are the key skills and qualifications needed to thrive in mathematical optimization?

To thrive in Mathematical Optimization, you need a strong background in mathematics, statistical modeling, and algorithm development, often supported by a degree in mathematics, operations research, engineering, or related fields. Proficiency with programming languages such as Python, MATLAB, or specialized optimization software (like Gurobi, CPLEX, or AMPL) is typically required. Strong analytical thinking, problem-solving skills, and the ability to communicate complex concepts clearly are critical soft skills for this role. These skills enable professionals to design effective solutions, interpret results, and convey recommendations to both technical and non-technical stakeholders.

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What cities are hiring for Mathematical Optimization jobs?

Cities with the most Mathematical Optimization job openings:

What are the most commonly searched types of Mathematical Optimization jobs?

The most popular types of Mathematical Optimization jobs are:

What states have the most Mathematical Optimization jobs?

States with the most job openings for Mathematical Optimization jobs include:

Infographic showing various Mathematical Optimization job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $55,794 per year, or $26.8 per hour.

Principal Decision Scientist, Applied Optimization and Simulation 2026 - US (Remote)

Aimpoint Digital

Atlanta, GA • Remote

Full-time

Re-posted 20 days ago


Job description

Aimpoint Digital is an AI and data consulting firm that turns AI ambition into production reality, built on the data and analytics foundation required to scale. This position is within our decision sciences practice which focuses on delivering production solutions via mathematical optimization and machine learning for our Federal customers. This role requires an active security clearance and a willingness to work on SIPR 2-4 days per week depending on project needs. 

What you will do

As a part of Aimpoint Digital, you will focus on enabling our Federal clients to get the most out of their data. Our Decision Science practice focuses on business, data, and process understanding to identify the best approach to solve our client's complex problems. We focus on delivering tangible value with models in production, not chasing theoretical boundaries with prototypes. Typical solutions will utilize machine learning, artificial intelligence, statistical analysis, automation, optimization, and data visualizations. As a Lead Decision Scientist you will be expected to work independently on client engagements, manage tasking for more junior decision scientists, take part in the development of our practice, aid in business development, and contribute innovative ideas and initiatives to our company. As a Lead Decision Scientist you will:

  • Become a trusted advisor working with clients to design and build end-to-end analytical solutions from initial solution architecture design through feature engineering, modeling, deployment, and maintenance
  • Work independently to solve complex decision science use-cases across various industries using mathematical optimization, simulation, machine learning, statistical/mathematical modeling, and analytics to solve use cases for our federal clients
  • Use your experience with decision science to build and manage agentic workflows to complete modeling tasks
  • Flexibly lead projects, from hands-on single developer engagements through complex projects coordinating between account management and a team of developers across Aimpoint
  • Manage or mentor junior decision scientists through their career at Aimpoint
  • Synthesize insights and construct narratives to influence decision making using optimization, simulation, statistics, ML modeling, and other techniques
  • Write code in Python following software engineering best practices
  • Take models from development to production in client environments following DecisionOps/MLOps best practices
  • Collaborate with stakeholders and customers to ensure successful project delivery
  • Assist with technical proposal and GTM material development
  • Contribute to the Aimpoint perspective on agentic and AI accelerated decision science
  • Lead and deliver internal practice development initiatives

Who we are looking for

We are looking for collaborative individuals who want to drive value, work in a fast-paced environment, and solve real business problems.  You are a coder who uses AI to write efficient and optimized code. You are a problem-solver who can deliver simple, elegant solutions as well as cutting-edge solutions that, regardless of complexity, your clients can understand, implement, and maintain. You genuinely think about the end-to-end machine learning pipeline as you generate robust solutions. You are both a teacher and a student as we enable our clients, upskill our teammates, and learn from one another. You want to drive impact for your clients and do so through thoughtfulness, prioritization, and seeing a solution through from brainstorming to deployment. In particular you have these traits:

  • Active Secret or higher security clearance
  • Willingness and ability to work on-site in a facility with SIPR access for 2-4 days per week
  • MS/PhD in Operations Research, Industrial Engineering, Computer Science, Mathematics, Engineering, or other STEM-related field
  • MS + 5-6 years practical experience
  • PHD + 4-5 years practical experience
  • Strong theoretical knowledge of optimization techniques, including linear programming and integer programming and/or dynamic programming and graph theory
  • Proficiency in a programming language such as Python, and/or proficiency in an optimization platform, AIMMS/AMPL/GAMS/Pyomo
  • Practical experience with open-source solvers and commercial solvers, such as Gurobi, CPLEX or XPRESS
  • Required competency in Python for data manipulation and modeling via classical ML methodologies
  • Demonstrated evidence of experience with end-to-end model development including but not limited to:
    • Requirements gathering
  • Solution design / architecture
  • EDA / data validation
  • Model development and testing
  • Model deployment
  • Model maintenance
  • Business user handoff / training
  • Experience communicating complex topics and results to high-level stakeholders. Strong written and verbal communication skills are required.
  • Self-starter with excellent communication skills, able to work independently, and lead projects, initiatives, and/or people


Want to stand out?

  • Consulting Experience
  • Databricks Machine Learning Associate or Machine Learning Professional Certification
  • Snowflake SnowPro Core Certification or SnowPro Advanced: Data Scientist Certification
  • Claude certification or experience to generate skills / agents for decision science tasks
  • Experience with mathematical optimization and data science for Federal clients
     

We are actively seeking candidates for full-time, remote work within the US.