Optimization Scientist

Recentive Analytics Inc.; Boston (MA); Hybrid; USD 90,000 - 130,000; Full time

Benefits offered by this job

Impactful work in leading sports teams

Collaborative culture promoting learning

Opportunities for growth and ownership

Job summary

Recentive Analytics Inc. is seeking an Optimization Scientist to join its Operations Research team in Boston. The ideal candidate will have 5+ years of experience in operations research, expertise in mathematical programming, and proficiency in Python. Responsibilities include developing advanced optimization models for scheduling and resource allocation and collaborating with various teams to implement these solutions. This role offers the chance to impact operational strategies in the sports and entertainment industries.

Qualifications

• 5+ years of experience in operations research and optimization model development.
• Strong expertise in mathematical programming and iterative problem-solving.
• Proficiency in Python for developing scalable optimization solutions.

Responsibilities

• Develop and deploy optimization models for scheduling and resource allocation.
• Collaborate with teams to integrate optimization models into production systems.
• Prototype models to address real-world optimization challenges.

Skills

Operations Research & Optimization

Mathematical Programming

Production-Ready Development in Python

Combinatorial Optimization

Solver Familiarity (e.g., Gurobi, CPLEX)

Job description

Optimization Scientist

Department: Engineering

Employment Type: Full Time

Location: SF / Boston / Remote (USA)

Description

We’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 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.

Why Work at Recentive

Impactful Work: Our technology and products directly shape operational strategies at leading sports teams and leagues, broadcasters, and other key players in the live events ecosystem.

Forward Thinking: Design and build forward‑thinking analytical solutions that blend creativity and engineering—leveraging advanced algorithms, optimization, and machine learning to solve complex, real‑world challenges.

Collaborative Culture: We’re a growing, tight‑knit data‑centered team that values knowledge‑sharing, learning, and innovation.

Growth & Ownership: Shape your career as a technical leader while helping build next‑generation analytical tools from the ground up.