Bachelor's or master's degree in operations research, Mathematics, Computer Science, or related field
5+ years of experience applying operations research techniques
Hands-on experience with OR-Tools, CP-SAT, or similar solvers
Strong foundation in linear programming, integer programming, or stochastic optimization
Proficiency in Java, Python, or C++
Experience with cloud platforms like AWS, GCP, or Azure
Experience modeling and solving workforce or resource scheduling problems
Responsibilities
Research, design, and develop optimization models for workforce planning and scheduling
Implement the schedule optimization engine using OR-Tools/CP-SAT
Own the constraint model; formalize business rules into a solvable model
Build the production-grade solver meeting latency targets
Define and validate schedule quality metrics for production monitoring
Build and execute tests for optimization algorithms ensuring performance
Collaborate with teams to integrate optimization models into the WFM platform
Benefits
Opportunity to build a modern, in-house Workforce Management platform
Hands-on role with direct impact on schedule optimization solutions
Collaboration with architects and engineering teams for enterprise-grade solutions
Opportunity to work with advanced optimization models and algorithms
Participation in a company transitioning to a full-time onsite working model
Full Job Description
Note: Fidelity will not provide immigration sponsorship for this position.
The Role
Fidelity is building a modern, in-house Workforce Management (WFM) platform. You will own and build a constraint-solver foundation for the Contact Center Workforce Management schedule optimization engine leveraging a CP-SAT/OR-Tools engine at enterprise scale and reliability. This role is deeply a hands-on role, you will design, develop, and implement advance optimization models and algorithms to solve complex workforce planning and scheduling problems.
Key Responsibilities
Research, design, and develop optimization models for contact center workforce planning, scheduling and resource allocation.
Design and implement the schedule optimization engine using OR-Tools/CP-SAT
Own the constraint model end to end: formalize business rule into a solvable, maintainable model.
Build the production-grade solver as a service within the WFM platform that meets latency targets for real forecast group volumes
Define and validate schedule quality metrics (coverage attainment, constraint violations, solve time, solution stability across re-solves) and instrument the service so quality can be monitored in production.
Build and execute tests and validation of optimization algorithms to deliver performance, scalability, and accuracy
Collaborate with architects and engineering teams to integrate optimization models into an enterprise-grade optimization engine of the workforce management platform.
Effectively communicate and present findings, methodologies, and results to engineering and business stakeholders.
Partner with workforce-planning and real-time-analyst stakeholders to validate that generated schedules are operationally usable.
Required Qualifications
Bachelor's of master's degree in operations research, Mathematics, Computer Science, or a related field.
5+ years of experience in applying operations research techniques to real-world problems
Hands-on production experience with OR-Tools, CP-SAT, or a comparable constraint-programming / mixed-integer-programming solver.
Strong foundation in linear programming, integer programming, or stochastic optimization.
Proficiency in Java, Python, or C++.
Experience with cloud platforms such as AWS, GCP, or Azure.
Experience working with large datasets and solving large scale optimization problems.
Direct experience modeling and solving workforce or resource scheduling problems.
Working knowledge of WFM domain.
Knowledge of distributed systems and event-driven architecture
Preferred Qualifications
Prior experience building scheduling or optimization capability at a WFM SaaS provider or a large in-house contact center operations team.
Familiarity with other solver technologies (e.g., Gurobi, CPLEX, OptaPlanner).
Experience in applying ML, AI, or Data Science
Fidelity's Onsite Working Model Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.
