The Discrete Planning Group is a part of the Autonomy Team for Hivemind Enterprise Product, focused on researching and developing state‑of‑the‑art algorithms that drive intelligent flight behaviors while accounting for an uncertain and dynamic world.
Research, design, and implement state‑of‑the‑art algorithms for optimal task allocation, scheduling and temporal sequencing of heterogeneous teams of autonomous vehicles
Solidify and improve existing C++ based mission planning software applicable across disparate vehicle types and compute platforms
Work with engineers, program managers, and product managers to define a technical roadmap for future autonomy solutions and SDK offerings
Breakdown a mission into assignable tasks based on agent capabilities such as navigating in contested and denied environments and adapting to mission changes in real‑time
Construct feasible and optimal action plans for distributed teams of autonomous vehicles that also minimize human operator workload
Requirements
PhD in Optimization or Operations Research
Master’s degree with 4 or more years of work in the same areas
Lead the design, development, and deployment of optimization models and services for large-scale contact center workforce planning, scheduling, and resource allocation.
Compensation
Competitive Pay and Benefits: enjoy a comprehensive compensation and benefits package, including health insurance, and a relocation program.
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.
Lead data science initiatives focused on optimization and machine learning to drive business value for Capital One’s Retail Bank team.
Locations (2)
McLean, Virginia, United States
New York, New York, United States
Compensation
The minimum and maximum full-time annual salaries for this role are listed below, by location. McLean, VA: $197,300 - $225,100 for Mgr, Data Science New York, NY: $215,200 - $245,600 for Mgr, Data Science
Responsibilities
Work with Product Managers and Business Analysts to design in-market experiments, validating assumptions before full-scale development.
Formulate & solve complex problems by translating ambiguous business challenges into structured mathematical problems and implementing optimization models.
Build & deploy scalable models, developing, testing, and deploying production-grade optimization algorithms and simulation models using Python and commercial/open-source solvers.
Collaborate cross-functionally with Product, Engineering, and Business teams to integrate optimization engines into existing software systems and workflows.
