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.

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