Senior Operations Research Scientist

Senior Operations Research Scientist Bruteforce AI Research Lab Ontario, CA Job Details Full-time $11,700 - $18,300 a month 1 hour ago Benefits Paid parental leave Health insurance Flexible spending account Paid time off Parental leave Professional development assistance Flexible schedule Life insurance Qualifications AI models Resource allocation Operations research MATLAB Operational analysis Computational research Data transformation pipeline development Applied Mathematics Predictive modeling analysis AI integration R Scalable systems Computational framework Computational modeling Master's degree Research project technical leadership Machine intelligence Math Doctor of Philosophy Model deployment Industrial Engineering Engineering research Systems engineering Data-driven problem-solving Algorithm design research Simulation tools Machine learning (ML) fundamentals Predictive analytics projects Senior level Research findings presentation Full Job Description About the

Role:

Omaha is a growing hub for advanced analytics, optimization, and AI-driven decision systems, and at Bruteforce AI Research Lab we apply that same level of rigor to solving complex, real-world operational challenges. We build high-impact mathematical models, simulation frameworks, and decision intelligence systems that improve how large-scale systems are planned and optimized. We are looking for a Senior Operations Research Scientist who will take ownership of advanced modeling initiatives and transform complex, ambiguous problems into structured, data-driven solutions. You are not just supporting analysis—you are leading the design of optimization frameworks that directly influence strategic decisions. You will work at the intersection of operations research, machine learning, and large-scale systems engineering, collaborating with technical teams and stakeholders to build solutions that perform under real-world constraints. Why Work With Us?

High Impact Work:

Your models and algorithms directly influence critical, real-world decision systems at scale.

Technical Depth:

Work on challenging optimization, simulation, and AI-driven problems where rigorous thinking is valued over routine execution.

Ownership & Autonomy:

You will define modeling approaches, experimentation strategies, and analytical direction with minimal bureaucracy.

Modern Tooling:

We use Python-based analytics ecosystems, optimization solvers, machine learning frameworks, and scalable data pipelines.

Career Growth:

Clear path toward Principal Scientist or Lead Research Scientist roles as the organization expands.

Culture of Rigor:

We value clarity, precision, and measurable outcomes in everything we build.

Key Responsibilities:

Advanced Optimization Modeling:

Design and implement operations research models including linear programming, integer programming, stochastic modeling, and simulation systems.

Algorithm Development:

Develop and refine computational methods for large-scale decision and optimization problems.

AI & ML Integration:

Apply machine learning techniques to enhance predictive and prescriptive analytics systems.

Systems Optimization:

Solve complex problems in scheduling, resource allocation, logistics, and planning under uncertainty. Data Engineering for

Modeling:

Transform raw datasets into structured, model-ready inputs for analysis and simulation.

Technical Leadership:

Lead research initiatives and guide best practices in modeling, validation, and experimentation.

Communication of Insights:

Clearly present technical findings, trade-offs, and recommendations to technical and non-technical stakeholders.

What We Are Looking For:

Experience:

5+ years in Operations Research, Applied Mathematics, Industrial Engineering, Data Science, or related quantitative fields.

Education:

Master's or PhD strongly preferred in a quantitative discipline.

Optimization Expertise:

Strong experience with linear programming, integer programming, heuristics, and simulation methods.

Programming Skills:

Advanced proficiency in Python; experience with R, MATLAB, or similar tools is a plus.

Machine Learning Knowledge:

Familiarity with applied ML techniques in decision-making systems.

Problem-Solving Ability:

Strong capability to structure ambiguous, real-world problems into solvable mathematical models.

Communication:

Ability to translate complex analytical results into clear, actionable insights.

Mindset:

Analytical, rigorous, and comfortable working in high-complexity environments with real-world constraints.

Pay:

$11,700.00 - $18,300.00 per month

Benefits:

Flexible schedule Flexible spending account Health insurance Life insurance Paid parental leave Paid time off Professional development assistance

Work Location:

Hybrid remote in Ontario, CA 91761