Optimization ScientistDepartment: Engineering
Employment Type: Full Time
Location: SF / Boston / Remote (USA)
DescriptionWe're looking for a
Optimization Scientist to join Recentive's core Operations Research team. In this role, you'll develop and deploy optimization models that solve complex scheduling, resource allocation, and decision-making problems for some of the biggest names in sports and entertainment. You'll work closely with engineering and data teams teams to design analytical solutions that drive measurable impact across industries.
What You'll Be Doing- Advanced Optimization Model Development: Designing and building advanced optimization models, leveraging techniques like integer programming, network flow, and metaheuristics.
- Cross-Functional Collaboration: Collaborating with cross-functional teams to define problems, develop solutions, and integrate optimization models into production systems.
- Real-World Application & Deployment: Prototyping and deploying models to solve real-world problems in areas like scheduling, resource optimization, and forecasting.
What We're Looking For- Operations Research & Optimization Expertise: 5+ years of experience in operations research and optimization model development in a production environment.
- Mathematical Programming Experience: Strong expertise in mathematical programming, including integer programming, decomposition methods, and cutting-plane methods.
- Combinatorial Optimization Experience: Experience tackling large-scale combinatorial optimization problems with creative modeling techniques.
- Production-Ready Development: Proficiency in Python for developing scalable, production-ready optimization solutions.
- Solver & Framework Familiarity: Hands-on experience with commercial optimization solvers (e.g., Gurobi, CPLEX) and open-source frameworks.
- Iterative Problem-Solving Approach: A results-driven mindset with the ability to prototype quickly and iteratively refine solutions.
