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Mathematical Modeling Jobs in California (NOW HIRING)

R&D Engineer

Sunnyvale, CA · On-site

$150K - $220K/yr

About the role: We're looking for highly skilled R&D Engineers with expertise in algorithms, mathematical modeling, and scientific computing. The R&D Engineer will perform in-depth research on high ...

Data Scientist

Palo Alto, CA · On-site

$118.10 - $274.60/hr

Deep understanding of mathematical modeling, statistical inference, and machine learning techniques applied to real‑world data. * Strong proficiency in Python and SQL; experience with data ...

We will do this primarily by building software and mathematical optimization models, not manual planning. The team is uniquely positioned to develop and optimize these capacity plans and scaleably ...

We will do this primarily by building software and mathematical optimization models, not manual planning. The team is uniquely positioned to develop and optimize these capacity plans and scaleably ...

Transform complicated business problems into mathematics modeling and provide data-driven solutions. Perform statistical analysis, including clustering, cross-session and panel data regression using ...

Transform complicated business problems into mathematics modeling and provide data-driven solutions. Perform statistical analysis, including clustering, cross-session and panel data regression using ...

Showing results 41-60

Mathematical Modeling information

See California salary details

$27.1K

$56K

$59.7K

How much do mathematical modeling jobs pay per year?

As of Aug 9, 2026, the average yearly pay for mathematical modeling in California is $55,980.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,200.00 and $58,700.00 per year, depending on experience, location, and employer.

What jobs use mathematical modeling?

Mathematical modeling is used in a variety of jobs such as data analyst, operations researcher, financial analyst, and systems engineer. These roles involve creating models to analyze data, optimize processes, or predict outcomes, often requiring skills in programming, statistics, and specialized software like MATLAB or R.

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 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.

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 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 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 MATLAB or Python are often required.

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 most commonly searched types of Mathematical Modeling jobs in California? The most popular types of Mathematical Modeling jobs in California are:
What are popular job titles related to Mathematical Modeling jobs in California? For Mathematical Modeling jobs in California, the most frequently searched job titles are:
What job categories do people searching Mathematical Modeling jobs in California look for? The top searched job categories for Mathematical Modeling jobs in California are:
What cities in California are hiring for Mathematical Modeling jobs? Cities in California with the most Mathematical Modeling job openings:
Infographic showing various Mathematical Modeling job openings in California as of August 2026, with employment types broken down into 77% Full Time, 19% Part Time, 2% Temporary, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $55,980 per year, or $26.9 per hour.

Machine Learning/Operations Research Engineer

Apple

Cupertino, CA

$150K - $277K/yr

Full-time

Medical, Dental, Retirement

Posted 11 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
The people here at Apple don’t just create products - they create the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple and help us leave the world better than we found it.
Description
With the explosive growth of Apple products we are creating new opportunities for individuals to work on the most exciting new technologies at Apple. We are seeking a machine learning/operations research engineer to apply advanced mathematical modeling, statistical analysis, and optimization algorithms to solve complex manufacturing challenges. Machine learning/operations research engineers on our team directly impact our factory throughput, supply chain strategies, and cost-reduction initiatives by transforming raw operational data into actionable, data-driven decisions.","responsibilities":"Production Optimization: Develop and implement mathematical models for optimization of capacity, yield, cycle times, costs, throughput, shop-floor layout usage, dynamic scheduling, and other factory and supply chain metrics.
Simulation Modeling: Build and maintain mathematical models for simulation to act as a "digital twin" of our assembly lines, identifying bottlenecks and testing "what-if" capacity scenarios.
Data fusion and analytics: Develop and implement data fusion techniques to integrate different operational data sources and generate actionable insights for manufacturing intelligence.
Preferred Qualifications
Proven experience in GenAI application building with agents and agentic workflows. Experience with LLM and LMM development and fine-tuning is a major plus.
Proficiency in using cutting-edge GenAI tools, i.e. Claude Code, Roo Code, etc.
Familiarity with distributed computing, cloud infrastructure, and orchestration tools, such as Kubernetes, Apache Airflow (DAG), Docker, Conductor, Ray for LLM training and inference at scale is a plus.
Minimum Qualifications
Master’s degree or PhD in Operations Research, Industrial Engineering, Management Science, Applied Mathematics, or related field.
Proficiency with solvers and modeling languages such as Gurobi, CPLEX, CP-SAT, Pyomo, and GAMS.
Hands-on experience with simulation platforms like Arena, FlexSim, SimPy, and AnyLogic.
Experience with machine learning platforms such as PyTorch and Scikit-learn.
Excellent communication and presentation skills; ability to explain complex statistical and mathematical theories to non-technical stakeholders in simple, universal language.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

What Apple employees say

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976