GreyOrange is a global leader in AI-driven robotic automation software and hardware, transforming
distribution and fulfillment centers worldwide. Our solutions increase productivity, empower growth and scale, mitigate labor challenges, reduce risk and time to market, and create better experiences for customers and employees. Founded in 2012, GreyOrange is headquartered in Atlanta, Georgia, with offices and partners across the Americas, Europe and Asia.
Our Solutions
The GreyMatter Multiagent Orchestration (MAO) platform provides vendor-agnostic fulfillment
orchestration to continuously optimize performance in real time: the right order, with the right bot and agent, taking the right path and action. Currently operating more than 70 fulfillment sites across the globe (with deployments of 700+ robots at a single site), GreyMatter enables customers to decrease their fulfillment Cost Per Unit by 50%, reduce worker onboarding time by 90% and optimize peak season performance.
In retail stores, our gStore end-to-end store execution and retail management solution supports omnichannel fulfillment, real-time replenishment, intelligent workforce tasking and more. Using real-time overhead RFID technology, the platform increases inventory accuracy up to 99%, doubles staff productivity, and enables an engaging, seamless in-store experience.
As an Operations Research Scientist at GreyOrange, you will play a pivotal role in designing, developing, and implementing advanced models and algorithms that enhance decision-making across various business functions. You will collaborate with cross-functional teams, including product development, engineering, to optimize processes, improve product performance, and solve complex problems using mathematical and computational methods. The ideal candidate has a deep understanding of optimization, statistical modeling, and machine learning, with a passion for solving real-world problems in a high-tech environment.
Roles and responsibilities
- Develop and apply advanced operations research models, including optimization, simulation, and stochastic models, to solve complex business challenges.* Work closely with engineering, data science, and product teams to integrate OR models into SaaS platforms, providing actionable insights to enhance product performance.* Design and implement optimization algorithms (e.g., linear programming, mixed-integer programming, and nonlinear optimization) to drive business efficiency and effectiveness.* Analyze large datasets and develop predictive models using statistical and machine learning techniques to improve decision-making processes.* Develop mathematical models, simulations, or optimization algorithms to represent real-world systems or problems. Use techniques like linear programming, dynamic programming, queuing theory, or game theory to create models.* Build simulation models to evaluate potential solutions and improve resource allocation, scheduling, and supply chain logistics.* Collaborate with stakeholders to understand business requirements and translate them into mathematical models and algorithmic solutions.* Collaborate with cross-functional teams such as engineering, product, and operations to implement solutions. Communicate findings, insights, and recommendations to non-technical stakeholders through reports, presentations, or data visualizations.* Communicate complex mathematical concepts and results to non-technical stakeholders in a clear and understandable manner.* Monitor the performance of implemented solutions and refine models over time based on feedback and new data. Stay updated with the latest advancements in operations research, machine learning, and optimization, and apply best practices to develop innovative solutions.* Document models, algorithms, and analytical processes for future reference and team knowledge sharing. Generate reports that summarize the outcomes of analyses and model performance.
Requirements/Qualifications
- Master’s or Ph.D. in Operations Research, Applied Mathematics, Industrial Engineering, Computer Science, or a related field.* Strong expertise in mathematical modeling, optimization techniques (linear programming, dynamic programming), and statistical analysis.* Proficiency in programming languages such as Python, R, C++, and experience with optimization tools (Gurobi, CPLEX, MATLAB).* Familiarity with data analysis, machine learning techniques, and simulation.* Excellent problem-solving skills and the ability to translate complex problems into actionable insights.* Strong collaboration skills and the ability to communicate complex concepts to non-technical stakeholders.* Experience managing or contributing to research projects in a fast-paced environment.