An Operations Research Engineer to join the Workforce Management (WFM) product stream, focusing on building optimization capabilities for workforce planning, forecasting, scheduling, and resource allocation for a large-scale contact center environment.
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.
An Operations Research Engineer to join the Workforce Management (WFM) product stream, focusing on building optimization capabilities for workforce planning, forecasting, scheduling, and resource allocation for a large-scale contact center environment.
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.
An Operations Research Engineer to join the Workforce Management (WFM) product stream and help build the optimization capabilities behind workforce planning, forecasting, scheduling, and resource allocation for a large-scale contact center environment.
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.
Apply operations research, mathematical optimization, and algorithm design to solve complex workforce planning problems at enterprise scale for a large-scale contact center environment.
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.
An Operations Research Engineer to join the Workforce Management (WFM) product stream and help build the optimization capabilities behind workforce planning, forecasting, scheduling, and resource allocation for a large-scale contact center environment.
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.
An Operations Research Engineer to join the Workforce Management (WFM) product stream and help build the optimization capabilities behind workforce planning, forecasting, scheduling, and resource allocation for a large-scale contact center environment.
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, mentor, and lead a high-performing team of optimization engineers and quantitative specialists, fostering technical excellence, collaboration, accountability, and strong software engineering practices.
Responsibilities
Lead the design and implementation of mathematical models and algorithms using linear programming, mixed-integer programming, constraint programming, combinatorial optimization, and heuristic methods.
Deliver reliable, scalable optimization solutions using Python and tools such as Google OR-Tools, Gurobi, or CPLEX, integrating them into enterprise systems and automated workflows.
Partner with AI and Automation Engineering, Data Engineering, and business stakeholders to identify opportunities, define requirements, and operationalize algorithmic decision-making.
Prioritize initiatives based on business value and technical feasibility, measure outcomes, and establish a roadmap for reusable optimization capabilities that advance Crane’s strategy as an algorithmically driven logistics organization.
Requirements
Bachelor’s degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or a related quantitative field
Advanced degree in a relevant quantitative discipline
Lead the team responsible for the technology and infrastructure behind large-scale mathematical optimization in Maestro, focusing on making optimization faster, more scalable, and more reliable.
Locations (2)
Ottawa, Ontario, Canada
Toronto, Ontario, Canada
Responsibilities
Lead, mentor, and grow a team of engineers
Set clear expectations, provide regular feedback and coaching
Provide hands-on technical leadership across solver integration, architecture, performance, scalability, and production reliability
Evolve the platform supporting mathematical optimization, including solver interfaces, distributed execution, deployment, monitoring, and diagnostics
Drive solve acceleration through GPU computing, advanced solver technologies, improved hardware utilization, and AI/ML approaches
Build optimization, machine learning and decision-support solutions that improve network performance, operational efficiency and service reliability while reducing costs.
Responsibilities
Develop real-time, tactical and strategic network disruption management and recovery models to support railroad operations and service resiliency.
Provide production support, maintenance and ongoing enhancements for existing operations research applications.
Design, develop and implement optimization, simulation and machine learning models in Python, Java or C++.
Evaluate the accuracy, completeness and quality of data sources and resolve data quality issues and gaps.
Partner with business teams across departments to identify operational and process improvement opportunities.
Requirements
Master's or Ph.D. in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Computer Science, Information Systems, Data Science or a related quantitative field.
As the principal technical leader for Server Demand Planning across Infrastructure, you own the demand side of the plan and the feasible supply requirements it drives: you forecast long- and near-term server capacity demand by rack/hardware type and region, and you build the operations-research models that match that demand to supply so we land the right servers, in the right place, at the right time.
Responsibilities
Own the multi-horizon server/MW demand forecast long range (2-5+ years), by resource type and region, and the DC supply requirements it drives
Aggregate and normalize demand signals from short term demand and product groups, into a single trusted statistical long term demand plan
Formulate and solve the demand-supply matching problem using operations-research models that reconcile forecasted demand with hardware roadmaps, cooling, lead times, and power constraints to inform the Plan of Record Build across the full modeling spectrum: production-grade optimization and forecasting systems and prototype models and heuristics that answer a leadership question or unblock a decision
Set functional standards: define the operating model, bridge short- and long-term forecasts, track accuracy and supply-matching metrics, and close forecast-to-actual gaps
Partner with data center engineering, site selection, and hardware strategy teams to align next-generation data center designs, rack sizing, and hardware roadmaps with demand, eliminating stranded power, cooling, and space capacity
Requirements
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Manager, Solver InfrastructureKinaxis Inc. · Remote, Canada+1 more
Research Scientist, Server Demand ForecastingMeta · Menlo Park, US