ZipRecruiter

Log In

1

Mathematical Modeling Jobs in Texas (NOW HIRING)

next page

Showing results 1-20

All JobsMathematical Modeling JobsMathematical Modeling Jobs in Texas

Mathematical Modeling information

See Texas salary details

$25.6K

$52.8K

$56.4K

How much do mathematical modeling jobs pay per year?

As of Aug 25, 2026, the average yearly pay for mathematical modeling in Texas is $52,846.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,000.00 and $55,400.00 per year, depending on experience, location, and employer.

What is mathematical modeling?

Mathematical modeling is the process of using mathematical concepts, structures, and equations to represent real-world systems, phenomena, or problems. This can involve creating formulas or simulations to predict outcomes, analyze situations, or solve complex issues in fields like science, engineering, economics, and more. By abstracting key components of a problem into mathematical terms, models help researchers and professionals test ideas, optimize solutions, and make informed decisions. Mathematical modeling often requires both theoretical knowledge and practical application to ensure the model accurately reflects reality.

How to get a job in mathematical modeling?

The qualifications that you need to start working in mathematical modeling include a degree and experience using computer software and programming languages. You can start in this field by earning a bachelor’s degree in math, statistics, or computer science. Some employers accept applicants who have previous experience and relevant computation skills. If your duties involve computer programming, you need to know languages like Python or C++. Research positions often require a master’s degree or Ph.D. If your responsibilities include data analysis, you can pursue a graduate degree in data science, machine learning, or a similar subject.

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

To excel as a Mathematical Modeler, you need a strong background in mathematics, statistics, and computational science, typically supported by a degree in mathematics, engineering, or a related field. Familiarity with programming languages such as Python, MATLAB, or R, and experience with modeling software and data analysis tools are crucial. Analytical thinking, problem-solving, and effective communication skills help translate complex findings for diverse stakeholders. These abilities ensure accurate model development, insightful analysis, and impactful decision-making across scientific and business applications.

What are some common challenges faced by professionals in mathematical modeling roles, and how can they be addressed?

Professionals in mathematical modeling often encounter challenges such as dealing with incomplete or noisy data, ensuring models are both accurate and interpretable, and effectively communicating complex results to non-technical stakeholders. To address these issues, it's important to regularly validate models with real-world data, collaborate closely with domain experts, and develop strong data visualization and presentation skills. Building a robust understanding of statistical methods and staying updated on new modeling techniques can also help in overcoming these challenges and delivering impactful results.

What is the difference between Mathematical Modeling vs Data Analyst?

AspectMathematical ModelingData Analyst
Required CredentialsDegree in Mathematics, Applied Math, or related fieldsDegree in Statistics, Data Science, or related fields
Work EnvironmentResearch labs, engineering firms, academiaBusiness, finance, marketing departments
Industry UsageDeveloping models to simulate systems or processesAnalyzing data to inform business decisions

Mathematical Modeling focuses on creating mathematical representations of real-world systems, often for simulation or prediction. Data Analysts interpret and analyze data sets to support decision-making. While both roles require strong quantitative skills and familiarity with statistical tools, Mathematical Modelers emphasize developing models, whereas Data Analysts focus on data interpretation and reporting.

What do you do in mathematical modeling?

In mathematical modeling, a mathematical modeler develops mathematical representations of real-world systems to analyze and predict their behavior. This involves formulating equations, using computational tools, and validating models with data to support decision-making or problem-solving. Strong analytical skills and knowledge of programming languages like Python or MATLAB are often essential.
More about Mathematical Modeling jobs

What are the most commonly searched types of Mathematical Modeling jobs in Texas?

The most popular types of Mathematical Modeling jobs in Texas are:

What are popular job titles related to Mathematical Modeling jobs in Texas?

For Mathematical Modeling jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Mathematical Modeling jobs in Texas look for?

The top searched job categories for Mathematical Modeling jobs in Texas are:

What cities in Texas are hiring for Mathematical Modeling jobs?

Cities in Texas with the most Mathematical Modeling job openings:

Mathematical Modeling jobs near you
Infographic showing various Mathematical Modeling job openings in Texas as of August 2026, with employment types broken down into 85% Full Time, 11% Part Time, 3% Contract, and 1% Nights. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $52,846 per year, or $25.4 per hour.

R&D Data Scientist: Mathematical Modeling and Optimization

Liftlab Analytics, Inc.

Austin, TX • Remote

Full-time

Re-posted 10 days ago

Job description

(Fully-remote US position)
About LiftLab

Liftlab is the leading provider of science-driven software to optimize marketing spend and predict revenue for optimal spend levels. We call this the Science of Marketing Effectiveness. Our platform combines economic modeling with specialized media experimentation so brands and agencies can clearly see the tradeoffs of growth and profitability. With decades of experience in marketing analytics and data science, our team of industry experts and thought leaders is proud to enable leading and emerging brands such as Cinemark, Express, Hanna Anderson, Lulu & Georgia, Pandora, Sephora, Skims, Tory Burch, Thrive, and Vionic, with our cutting-edge solutions and strategic guidance.

Job responsibilities

  • Develop new algorithm-based features of LiftLab's marketing measurement and optimization platform

  • Performs diagnostics and root-cause analysis and provide fixes

  • Works with Data Science and Engineering to implement these features into LiftLabs product and workflow

Course work/experience:

  • Data manipulation

    • SQL

    • Operating on big datasets in Python

    • Data visualization

  • Mathematical optimization

    • Linear optimization concepts

    • Nonlinear continuous optimization

    • Linear algebra

  • Mathematical modeling

    • Using parametrized systems of equations to represent real-world systems

  • Statistics

    • Multivariate regression

    • Clear understanding of Maximum Likelihood estimation and computational methods to find MLE parameters

    • Bayesian concepts

    • Hypotheses testing

Education requirements

Graduate degree in Applied Mathematics, Scientific Computing, Operations Research or related field. We will consider holders of Bachelor degrees with relevant experience

Skills/Aptitude

  • Engineering and detective mindset

    • Both to diagnose data and existing algorithms and to develop new analytics functionality

  • Pragmatic approach to real-world problems

  • Focus on problem solving over applying specific models

  • Willingness to make approximations and assumptions rather than find "the" optimal solution

  • Ability to combine multiple techniques and models to solve end-to end-problems

  • Communication and collaboration skill

  • Ability to convert non-technical requests into project specifications

Apply