BosonQ Psi

Math Optimization Researcher

BosonQ Psi is seeking a highly skilled and motivated Math Optimization Researcher to join our team. The ideal candidate will play a pivotal role in developing, implementing, and fine-tuning cutting-edge mathematical models and algorithms to solve complex optimization problems across diverse industries, such as aerospace, manufacturing, supply chain, and resource allocation. You will work closely with cross-functional teams, leveraging your expertise in optimization theory and algorithm development to address real-world challenges. This role requires proficiency in coding (Python, C++, Matlab) and hands-on experience with optimization libraries like SciPy, NumPy, Gurobi, and CPLEX.

Roles & Responsibilities

• Develop and integrate cutting-edge mathematical models and algorithms (LP, IP, MIP, NLP, QP) for industries such as aerospace, manufacturing, supply chain, and resource allocation.
• Fine-tune optimization solutions to ensure performance, scalability, and real-world applicability.
• Collaborate with cross-functional teams to understand business requirements, analyze data, and set optimization goals.
• Design and execute experiments to test and validate the effectiveness of optimization algorithms.
• Share insights and recommendations with stakeholders through clear, compelling, and actionable communication.
• Develop and implement various optimization problems, including simulation benchmarking and use-case development.
• Perform thorough code testing, debugging, and performance benchmarking.
• Contribute to the creation and preparation of technical documentation.
• Stay up to date with emerging optimization techniques and incorporate them into solutions.
• Analyze large datasets and apply distributed computing techniques as necessary.

Requirements

Must Have:

• Minimum 3+ years of experience in an optimization or operations research role.
• Proficiency in coding languages such as Python, C++, Matlab, and experience with optimization libraries like SciPy, NumPy, Gurobi, or CPLEX.
• Strong grasp of optimization theory and algorithm development.
• Deep theoretical knowledge in at least one of the following areas: combinatorial optimization, statistical learning, complexity theory, or approximation algorithms.
• Analytical and problem-solving skills to turn complex optimization challenges into actionable solutions.
• Experience working with large datasets and distributed computing systems.
• Excellent communication skills with the ability to collaborate effectively within a team environment.
• A Master's degree in Computer Science, Optimization, Operations Research, Applied Mathematics, Engineering, or a relevant field.

Good to Have:

• A PhD in a relevant field (Optimization, Operations Research, Applied Mathematics, etc.).
• Industry experience in aerospace, automotive, or manufacturing sectors.