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Stochastic Processes Jobs (NOW HIRING)

Artech

US Application Developer

Dallas, TX · On-site

... Stochastic Processes, Forecasting, Casual Inference, Nonlinear Dynamics, Chaos Theory Please apply on our company website ( www.artechinfo.com ) with reference to job ID, or contact me at riya.negi ...

pony.ai

Software Engineer Intern - Generalist

Fremont, CA · On-site

$7K - $10K/mo

Strong foundation in mathematics and theoretical computer science, such as linear algebra, calculus, graph theory, computational geometry, combinatorial optimization algorithms, stochastic processes ...

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How much do stochastic processes jobs pay per year?

As of Jun 4, 2026, the average yearly pay for stochastic processes in the United States is $84,057.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,000.00 and $97,000.00 per year, depending on experience, location, and employer.

What is a Stochastic Processes job?

A Stochastic Processes job typically involves working with mathematical models that incorporate randomness and uncertainty. Professionals in this field analyze and apply probabilistic methods to various domains, such as finance, engineering, machine learning, and operations research. Common tasks include developing statistical models, simulating random systems, and optimizing decision-making under uncertainty. These roles often require expertise in probability theory, programming, and statistical analysis.

What are the key skills and qualifications needed to thrive in the Stochastic Processes position, and why are they important?

To excel in a Stochastic Processes role, you need a strong background in probability theory, mathematical modeling, statistical analysis, and typically a degree in mathematics, statistics, or a related quantitative field. Proficiency with tools such as MATLAB, R, Python, or specialized simulation software, as well as relevant certifications in data analysis or quantitative finance, is often required. Strong problem-solving abilities, attention to detail, and the ability to communicate complex concepts clearly are key soft skills in this field. These skills are vital to effectively analyze random systems, model uncertainty in real-world scenarios, and help guide data-driven decision-making across numerous industries.

What kinds of projects or problems do professionals working in stochastic processes typically address?

Professionals specializing in stochastic processes commonly tackle projects that involve modeling and predicting random systems, such as financial markets, inventory management, telecommunications networks, or biological processes. Typical responsibilities include developing and testing mathematical models, running simulations, analyzing large datasets, and providing actionable insights to support strategic business decisions. They often collaborate closely with data scientists, engineers, or business analysts depending on the industry, and their work can have a direct impact on optimizing operations or managing risk. Project scopes can range from short-term analyses to longer-term research and development initiatives, offering a dynamic and intellectually stimulating work environment.
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Stochastic Processes jobs near you
Infographic showing various Stochastic Processes job openings in the United States as of May 2026, with employment types broken down into 12% Locum Tenens, 35% As Needed, 2% Full Time, 27% Temporary, and 24% Nights. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution, with an average salary of $84,057 per year, or $40.4 per hour.
Post Doc Research Associate

Post Doc Research Associate

Purdue University

West Lafayette, IN • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Purdue University rating

7.5

Company rating: 7.5 out of 10

Based on 132 frontline employees who took The Breakroom Quiz

263rd of 532 rated colleges and universities

Job description

Req Id: 42032
Job Title: Post Doc Research Associate
City: West Lafayette
Job Description:
Job Summary
Postdoctoral Position in Theoretical & Computational Ecology and Evolution of Infectious Diseases
The He Lab at Purdue University (https://scholar.google.com/citations?user=WajKQtsAAAAJ&hl=en) is seeking a highly motivated postdoctoral researcher to join a project at the interface of theoretical disease modeling, molecular epidemiology and population dynamics. The position focuses on advancing a newly developed numerical framework that characterizes disease strain dynamics under antigenic diversification, immune-mediated selection, and transmission dynamics. The work will involve a combination of analytical modeling of stochastic processes and agent-based stochastic simulations.
The position is funded for two years, with an ideal starting date from June, 2026. If you are interested or have questions, please apply online. Please provide a document describing your research interests, past research experience, and the fit, and attach your CV and contact info for 2-3 references in one PDF. Review of applications will begin immediately and continue until the position is filled.
We offer:
- Freedom to develop independent research directions within the project scope
- Opportunities to interact and collaborate with other research groups working on the theory of eco-evolutionary dynamics
- Opportunities to collaborate on more applied projects in the lab
- Competitive salary and benefits aligned with NIH standards
Education
Required Qualifications
- PhD in a relevant field (e.g., theoretical ecology/evolution, applied mathematics, epidemiology, physics, computational biology, or related discipline)
Experience
Required Qualifications
- Strong background in mathematical modeling of biological systems, especially dynamical systems or stochastic processes
- Experience with computational modeling (e.g., Python, R, Julia, or C++)
- Familiarity with at least one of the following: Infectious disease modeling; population genetics; metapopulation models
- Demonstrated ability to conduct independent research and publish in peer-reviewed journals
Preferred Qualifications
- Knowledge of basic immunology
- Interest in linking theoretical predictions to empirical disease estimations
FLSA Status
Exempt

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