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Mathematical Modeling Jobs in Berkeley, CA (NOW HIRING)

Amazon

Data Scientist II - AMZ27351.1

San Francisco, CA · On-site

$175K - $212K/yr

Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic ...

Mytra

Senior Modeling & Optimization Engineer

Brisbane, CA · On-site

$125K - $172K/yr

... mathematical optimization, statistical modeling, or applied data science. • Ability to design experiments, analyze data, and communicate insights clearly to technical and non-technical audiences ...

Mytra

Senior Modeling & Optimization Engineer

Brisbane, CA · On-site

$175K - $190K/yr

Experience with at least two of: discrete-event simulation, mathematical optimization, statistical modeling, or applied data science. * Range, and an appetite for it: this role flexes across analysis ...

Mytra

Senior Modeling & Optimization Engineer

Brisbane, CA · On-site

$175 - $190/hr

Experience with at least two of: discrete‑event simulation, mathematical optimization, statistical modeling, or applied data science. * Ability to design experiments, analyze data, and communicate ...

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All JobsMathematical Modeling JobsMathematical Modeling Jobs in Berkeley, CA

Mathematical Modeling information

See Berkeley, CA salary details

$33.7K

$69.5K

$74.1K

How much do mathematical modeling jobs pay per year?

As of Aug 9, 2026, the average yearly pay for mathematical modeling in Berkeley, CA is $69,454.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,200.00 and $72,900.00 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.

More about Mathematical Modeling jobs
What are popular job titles related to Mathematical Modeling jobs in Berkeley, CA? For Mathematical Modeling jobs in Berkeley, CA, the most frequently searched job titles are:
What job categories do people searching Mathematical Modeling jobs in Berkeley, CA look for? The top searched job categories for Mathematical Modeling jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Mathematical Modeling jobs? Cities near Berkeley, CA with the most Mathematical Modeling job openings:
Mathematical Modeling jobs near you
Infographic showing various Mathematical Modeling job openings in Berkeley, CA as of August 2026, with employment types broken down into 84% Full Time, 14% Part Time, and 2% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $69,454 per year, or $33.4 per hour.

Principal Scientist, Cancer Pharmacology, Translational Research

Revolution Medicines

Redwood City, CA • Hybrid

Full-time

Re-posted 19 days ago

Job description

The Opportunity:

As a Principal Scientist in the Cancer Pharmacology team within the Translational Research group, in the Biomedical Discovery Research Department, you will:

  • Provide scientific and strategic leadership for RAS(ON) inhibitor programs by translating mechanistic insights in RAS biology into impactful pharmacology strategies and translational study plans.

  • Lead, mentor, and develop a high-performing group of in vivo scientists, fostering scientific excellence, innovation, and cross-functional collaboration.

  • Drive the design, execution, and interpretation of in vivo efficacy and PK/PD studies conducted internally and through external partners to advance oncology discovery and development programs.

  • Partner closely with the cross-functional quantitative modeling group to provide biological insight, translational context, and critical evaluation supporting PK/PD modeling efforts.

  • Influence portfolio progression through generation of high-impact data packages and contributions to regulatory submissions.

  • Serve as the in vivo pharmacology expert on cross-functional project teams, partnering closely with colleagues in Chemistry, Discovery Biology, DMPK, and Toxicology to build cohesive preclinical data sets.

  • Communicate scientific strategy, key findings, and program recommendations to cross-functional teams and senior leadership to drive informed decision making.

Required Skills, Experience and Education:

  • Ph.D. in Pharmacology or Cancer Biology, or a related scientific discipline with direct relevance to oncology drug development.

  • 5+ years of relevant industry experience in oncology drug discovery and development.

  • Demonstrated scientific leadership, strategic thinking, and problem-solving skills in advancing oncology drug discovery programs.

  • Deep expertise in tumor biology, RAS signaling pathways, and translational oncology pharmacology.

  • Extensive hands-on experience in the design, analysis, and interpretation of in vivo oncology studies and disease models.

  • Strong understanding of translational PK/PD concepts and integration of efficacy, biomarker, and pharmacokinetic data to support translational hypotheses.

  • Working knowledge of modeling approaches, including PBPK, QSP, and semi-mechanistic PK/PD frameworks.

  • Proven track record of leading, mentoring, and developing scientific talent within collaborative, matrixed research environments.

  • Excellent written and verbal communication skills with the ability to effectively communicate complex scientific concepts to both specialist and non-specialist audiences.

  • Demonstrated track record of scientific innovation, productivity, and collaboration in a dynamic and fast-paced drug discovery environment.

Preferred Skills:

  • Experience with RAS-targeted therapies and diverse oncology therapeutic modalities, including small molecules, antibodies, and antibody-drug conjugates (ADCs).

  • Strong theoretical understanding of the concepts, assumptions, and limitations associated with PK/PD and translational mathematical modeling.

  • Experience contributing to IND-enabling activities and regulatory documentation is desirable.

  • Demonstrated ability to influence scientific strategy and effectively operate within highly collaborative cross-functional teams.

#LI-Hybrid   #LI-NE1

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