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.
As a Manager Data Science, you will spearhead the development and execution of advanced analytical strategies that drive business insights and operational excellence across multiple domains, leading a cross-functional team of data scientists, analysts, and engineers.
Compensation
Rs 2000000 - Rs 4200000 (i.e. INR 20-42 LPA)
Responsibilities
Lead the design, development, and deployment of predictive models using machine learning and statistical techniques.
Oversee end-to-end data science projects, from hypothesis generation to production-grade solution delivery.
Drive the adoption of Bayesian methods to enhance probabilistic inference and decision-support systems.
Mentor and coach a team of data scientists, fostering a culture of continuous learning and innovation.
Collaborate with product, engineering, and business leaders to translate data insights into actionable strategies.
As a Sr Data Scientist, you will spearhead advanced analytical initiatives that fuse Operations Research, Natural Language Processing, and Deep Learning to unlock actionable insights and drive decision‑making across multiple domains.
As a Senior Specialist I, Business Analytics, you will turn complex supply-chain data into decisions that help ASM keep critical parts available for our customers while improving inventory, cost, and lead-time performance. You will own and develop forecasting, inventory, capacity, and optimization models, working across Spares Supply Chain, Operations, Procurement, Logistics, Service, Finance, and IT to solve real operational challenges.
Responsibilities
Develop demand forecasts using historical demand, installed-base data, customer commitments, service requirements, equipment plans, consumption patterns, and other relevant business drivers.
Apply statistical and machine-learning methods to intermittent, lumpy, seasonal, and rapidly changing demand across parts, product families, customers, regions, warehouses, and suppliers.
Build expected, upside, downside, and constrained-demand scenarios, and evaluate forecast performance using measures such as WAPE, MAE, forecast bias, Forecast Value Added, and service-level outcomes.
Translate forecasts into practical recommendations for purchasing, inventory, supplier capacity, and allocation decisions.
Design inventory policies covering safety stock, reorder points, replenishment parameters, target stock levels, and order quantities to balance availability, working capital, obsolescence risk, and logistics cost.
The role involves building and extending large-scale simulations of global traffic placement, solving capacity planning as constrained optimization, building production forecasting for peak demand, and leading technical direction across multiple teams.
Responsibilities
Build and extend large-scale simulation of global traffic placement: how traffic shifts across edge sites as demand grows and as sites, peering links and backbone paths fail.
Work with the load-balancing team on simulation fidelity and performance, and validate results against production behavior and live failure tests
Formulate and solve capacity planning as constrained optimization: where to add compute, peering and caches, given space, power, supply, latency and failure constraints.
Build and tune the solvers
Plan capacity for the browsers, proxies and caches Meta's services and AI agents use to reach the internet, and find which one becomes the bottleneck as Meta's internet access needs grow.
Utilizing problem solving and quantitative methods to provide engineering solutions through projects in various areas of the organization.
Compensation
This position may be eligible for annual bonus and incentives based on profitability or volumes in accordance with the terms of the Company’s bonus and incentive plans
Responsibilities
Apply knowledge of organization to make improvements to existing processes or solutions
Increase awareness and knowledge of quantitative methods, computer programming, optimization, and/or solution design to support data analysis
Solve problems related to process improvement by analyzing internal and external data, including but not limited to operations, pricing, financial, and customer information
Analyze a variety of data and processes to reduce cost and increase business productivity
Communicate with business partners to understand problems and present solutions
Requirements
Bachelor's degree in Industrial Engineering
Bachelor's degree in Management Information Systems
Advisory Technical Support Engineer specializing in advanced technologies for production planning, scheduling, blending and optimization within the downstream sector.
Responsibilities
Collaborate with customers to resolve technical issues
Deliver expert guidance and training on AspenTech solutions
Troubleshoot and resolve complex technical and engineering problems
Escalate customer issues and drive cross‑functional resolution
Develop and maintain customer focused training materials
We are looking for a highly skilled Applied Optimisation Scientist to design, extend, and validate large-scale mathematical optimisation models supporting strategic mine planning and optimisation platforms.
Responsibilities
Develop and maintain large-scale LP/MIP optimisation models for mine planning and scheduling.
Design and implement constraints including capacity, sequencing, blending, cost optimisation, and boundary conditions.
Support advanced optimisation workflows such as Direct Block Scheduling (DBS).
Continuously improve model structure and performance for production-grade deployments.
Integrate and configure optimisation solvers such as Gurobi, Maroma, and AMPL.
Requirements
Bachelor’s or master’s degree in operations research
Operations Research Engineers function as internal consultants to support improved decision-making by developing models using operations research methods, such as mathematical optimization and stochastic simulation.
Responsibilities
Participate in the scoping of business problems to apply operations research methods
Work with key contacts to identify and translate data requirements which represent the system and constraints to be modeled
Develop custom mathematical optimization and/or stochastic simulation models by applying: linear programming, mixed-integer programming, system dynamics simulation, and/or discrete event simulation
Proactively drive cross-functional alignment across organizations to validate model solution quality
Create documentation to explain how the operations research methods are applied, and conduct reviews with senior technical professionals to ensure technical quality
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.
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