Senior Engineer - Operations Research

As an Operations Research Scientist at GreyOrange, you will be responsible for designing, developing, and implementing advanced models and algorithms to improve decision-making processes across various business functions. Your role will involve collaborating with cross-functional teams, including product development and engineering, to optimize processes, enhance product performance, and solve complex problems using mathematical and computational methods. The ideal candidate for this position should have a deep understanding of optimization, statistical modeling, and machine learning, along with a passion for addressing real-world challenges in a high-tech environment. You will be expected to: - Develop and apply advanced operations research models, such as optimization, simulation, and stochastic models, to address complex business challenges. - Collaborate with engineering, data science, and product teams to integrate operations research models into SaaS platforms for actionable insights. - Design and implement optimization algorithms (e.g., linear programming, mixed-integer programming, nonlinear optimization) to drive business efficiency and effectiveness. - Analyze large datasets and create predictive models using statistical and machine learning techniques to enhance decision-making processes. - Build mathematical models, simulations, and optimization algorithms to represent real-world systems or problems, using techniques like linear programming, dynamic programming, queuing theory, or game theory. - Construct simulation models to evaluate potential solutions and improve resource allocation, scheduling, and supply chain logistics. - Work with stakeholders to understand business requirements and translate them into mathematical models and algorithmic solutions. - Collaborate with cross-functional teams to implement solutions and communicate findings, insights, and recommendations to non-technical stakeholders through reports, presentations, or data visualizations. - Explain complex mathematical concepts and results in a clear and understandable manner to non-technical stakeholders. - Monitor solution performance, refine models based on feedback and new data, and stay updated on the latest advancements in operations research, machine learning, and optimization to develop innovative solutions. - Document models, algorithms, and analytical processes for future reference and team knowledge sharing, and generate reports summarizing analysis outcomes and model performance. Requirements: - Masters 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.,