Technical > Mathematical Modeler
Salary National Average
| Low | Medium | High |
|---|---|---|
| 64080.0000 | 81710.0000 | 100650.0000 |
Supply and Demand
Candidate Supply: 1,828 Job Openings: 320
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Short Description:
A Mathematical Modeler, also known as a Mathematical Model Developer or Mathematical Analyst, is a professional who specializes in creating mathematical models to simulate and analyze real-world phenomena. They play a crucial role in diverse fields, including physics, economics, engineering, and epidemiology, by designing and implementing mathematical models that help solve complex problems, make predictions, and inform decision-making.
Duties / Responsibilities:
- Collaborate with experts and stakeholders to understand and define the problem or system to be modeled.
- Develop mathematical models, including differential equations, agent-based models, and statistical models.
- Collect and preprocess data for model calibration and validation.
- Implement and run simulations to analyze the behavior of the model and generate insights.
- Interpret and visualize simulation results to provide actionable recommendations and insights.
- Fine-tune models based on feedback, validation, and real-world observations.
- Stay updated with advances in mathematical modeling techniques and tools.
- Provide guidance and support to interdisciplinary teams in utilizing mathematical models.
- Document model development processes, assumptions, and results for reporting and transparency.
- Communicate complex mathematical concepts and model findings to non-technical stakeholders.
Skills / Requirements / Qualifications
- Education: Bachelor's, master's, or Ph.D. in mathematics, applied mathematics, engineering, or a related field.
- Modeling Tools: Proficiency in mathematical modeling tools and software, such as MATLAB, Simulink, or specialized modeling software.
- Programming: Strong programming skills in languages like Python, R, or specialized modeling languages.
- Mathematics: Mathematical expertise, including knowledge of differential equations, optimization techniques, and statistical analysis.
- Analytical: Problem-solving and analytical abilities for formulating and solving complex mathematical problems.
- Communication: Excellent communication and collaboration skills for working with diverse teams and explaining mathematical concepts to non-experts.
- Industry Knowledge: Familiarity with the specific domain or industry in which the modeling is applied, as well as the ability to translate real-world problems into mathematical representations.
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