Math > Mathematical Programmer
Salary National Average
| Low | Medium | High |
|---|---|---|
| 93640.0000 | 116440.0000 | 126700.0000 |
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Candidate Supply: 14,093 Job Openings: 5,229
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Short Description:
A Mathematical Programmer, often known as an Optimization Scientist or Mathematical Optimization Engineer, is a professional who applies mathematical modeling and programming techniques to solve complex optimization problems. They play a crucial role in a wide range of industries, including logistics, finance, manufacturing, and transportation, by developing algorithms and software solutions to optimize decision-making processes and resource allocation.
Duties / Responsibilities:
- Formulate and model real-world problems as mathematical optimization or linear programming tasks.
- Develop algorithms and software applications to solve optimization problems efficiently.
- Collaborate with subject matter experts to understand and define the objectives and constraints of optimization projects.
- Implement and integrate optimization solutions into existing systems or workflows.
- Test and validate optimization models and algorithms, fine-tuning them for improved performance.
- Maintain and update optimization software to adapt to changing business requirements.
- Analyze and interpret optimization results to make data-driven decisions.
- Provide consultation and support to teams and stakeholders on optimization strategies and best practices.
- Stay current with advances in mathematical optimization, linear programming, and algorithm development.
- Communicate complex mathematical concepts and optimization results to non-technical stakeholders.
Skills / Requirements / Qualifications
- Education: Bachelor's, master's, or Ph.D. in mathematics, computer science, operations research, or a related field.
- Modeling Software: Proficiency in mathematical modeling and optimization software, such as CPLEX, Gurobi, or open-source tools like PuLP.
- Programming: Strong programming skills in languages like Python, Java, or C++.
- Mathematical Techniques: Knowledge of mathematical optimization techniques, linear programming, and integer programming.
- Analytical: Problem-solving and analytical abilities to formulate and solve optimization problems.
- Communication: Excellent communication and teamwork skills for collaboration with interdisciplinary teams and explaining optimization concepts to non-experts.
- Industry Knowledge: Familiarity with industry-specific optimization problems and constraints, depending on the sector of employment.
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