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Dynamic Stochastic Optimization Jobs (NOW HIRING)

Bank of America

Data Scientist I

New York, NY · On-site

$105K - $193K/yr

You will work across teams to solve problems in recommendation, stochastic optimization, and time ... Are energized by the high stakes and intensity of dynamic environments and ready to dive in ...

Bank of America

Data Scientist I

New York, NY

$105K - $193K/yr

You will work across teams to solve problems in recommendation, stochastic optimization, and time ... Are energized by the high stakes and intensity of dynamic environments and ready to dive in ...

Bank of America

Data Scientist I

Charlotte, NC

$105K - $193K/yr

You will work across teams to solve problems in recommendation, stochastic optimization, and time ... Are energized by the high stakes and intensity of dynamic environments and ready to dive in ...

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$16K

$55.8K

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How much do dynamic stochastic optimization jobs pay per year?

As of Jun 5, 2026, the average yearly pay for dynamic stochastic optimization in the United States is $55,794.00, according to ZipRecruiter salary data. Most workers in this role earn between $36,000.00 and $72,500.00 per year, depending on experience, location, and employer.

What are the typical collaboration opportunities for professionals working in Dynamic Stochastic Optimization roles?

Professionals in Dynamic Stochastic Optimization often collaborate closely with data scientists, software engineers, and domain experts to develop and implement optimization models that adapt to uncertainty over time. They may also work with decision-makers or operational teams to translate complex mathematical solutions into actionable strategies. This interdisciplinary collaboration helps ensure that the models are both mathematically sound and practical for real-world applications, such as supply chain management, finance, or energy systems.

What is dynamic stochastic optimization?

Dynamic stochastic optimization is a mathematical approach used to make optimal decisions over time in situations where outcomes are uncertain and may change. It combines dynamic programming (making a sequence of decisions) with stochastic modeling (accounting for randomness). This method is widely used in fields like finance, engineering, and operations research to solve problems such as investment planning, resource allocation, and supply chain management. By modeling uncertainties and the evolution of systems over time, it helps decision-makers find strategies that maximize expected performance or minimize risk.

What is the difference between Dynamic Stochastic Optimization vs Data Analyst?

AspectDynamic Stochastic OptimizationData Analyst
Required CredentialsAdvanced degrees in Operations Research, Mathematics, or related fieldsBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentQuantitative teams, research labs, or analytics departmentsBusiness units, marketing, finance, or operations teams
Industry UsageSupply chain, finance, energy, and logisticsMarketing, finance, healthcare, and retail sectors
Search & Comparison IntentUnderstanding optimization techniques for decision-making under uncertaintyAnalyzing data to inform business decisions

Dynamic Stochastic Optimization focuses on developing models to make optimal decisions in uncertain environments, often requiring advanced mathematical skills. Data Analysts interpret and analyze data to support business strategies. While both roles involve data, their applications, skills, and industries differ significantly.

What are the key skills and qualifications needed to thrive as a Dynamic Stochastic Optimization Specialist, and why are they important?

To thrive as a Dynamic Stochastic Optimization Specialist, you need a solid background in mathematics, statistics, operations research, and computer science, typically supported by an advanced degree in a quantitative field. Proficiency with programming languages (such as Python, MATLAB, or R), optimization software (like Gurobi or CPLEX), and familiarity with simulation tools are essential. Strong analytical thinking, creative problem-solving, and effective communication skills help you translate complex models into actionable insights. These competencies are crucial for designing, implementing, and communicating robust optimization models that drive decision-making in uncertain and dynamic environments.
Infographic showing various Dynamic Stochastic Optimization job openings in the United States as of May 2026, with employment types broken down into 2% Locum Tenens, and 98% Part Time. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution, with an average salary of $55,794 per year, or $26.8 per hour.

Operations Analyst III

Dynamic Solutions Technology LLC

San Diego, CA • On-site

Full-time

Posted 9 days ago

Job description

Dynamic Solutions Technology, LLC, a premier strategic services firm that meets IT and Service needs for commercial and government clients, is seeking full-time Operations Analyst III. This is an exempt position in support of the customer based in San Diego, CA.

Responsibilities:

  • Analyze and improve the coordination and management of large, complex organizations to optimize the use of funds, personnel, materials, and equipment.
  • Apply quantitative and analytical methods drawn from mathematics, science, and engineering to support data-driven decision-making.
  • Support strategic planning, forecasting, and resource allocation initiatives at both operational and enterprise levels.
  • Evaluate organizational performance through measurement frameworks, scheduling analysis, and system optimization techniques.
  • Assess and design production systems, facilities, supply chains, transportation, distribution, and pricing models.
  • Collect, validate, and analyze large datasets to identify trends, risks, and opportunities for efficiency gains.
  • Select and apply advanced analytical techniques, including simulation, linear and nonlinear programming, dynamic programming, queuing theory, stochastic models, and analytic hierarchy processes.
  • Develop mathematical and analytical models to represent real-world systems, clarify interdependencies, and test alternative scenarios to predict outcomes under varying conditions.

Desired Years of Experience/ Education:

  • Public Trust clearance
  • Bachelor's degree business field
  • 7 years of experience

Experience and Skills:

  • Excellent oral and written skills.
  • Excellent critical thinking skills.
  • Proficient in Microsoft applications such as Word, Excel, PowerPoint, and Outlook.
  • Ability to work independently and as a team member
  • Ability to learn and apply technical concepts to assigned duties

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