Senior Data Scientist, Operations Research

Responsibilities

• Develop ML and OR-based models for demand forecasting, assortment planning, purchase order optimization, inventory allocation and price optimization.
• Apply techniques such as mixed-integer programming, dynamic programming, graph theory, spatial optimization and simulation to solve real‑time decisioning problems.
• Integrate predictive ML models with optimization logic to enable adaptive, data‑driven decisions.
• Build and operationalize decision engines that automate fulfillment decisions across the enterprise.
• Collaborate with engineering to deploy models into production systems with real‑time data pipelines and monitoring.
• Ensure models are interpretable, auditable, and aligned with business constraints.
• Combine ML outputs with OR solvers via hybrid decision frameworks, enabling scenario‑aware optimization and policy simulation.
• Ensure robustness and scalability of models by leveraging containerized environments and observability tools.
• Enable real‑time decisioning by building & incorporating streaming pipelines and supporting low‑latency inference and optimization.
• Partner with product and operations to define decision boundaries, constraints, and success metrics.
• Communicate insights and model performance to technical and nontechnical audiences.
• Understand latest research in the field of OR and AI to give inputs to enterprise roadmaps to ensure we are on the path to build Best in Class merchandising planning and optimization solution.

Requirements

• Advanced degree (MS/Ph D) in Operations Research, Computer Science, Statistics, or related field.
• 4+ years of experience in building optimization and ML models in assortment planning, optimization, fulfillment or supply chain domains.
• OR Techniques: linear/mixed‑integer programming, simulation, queuing theory.
• ML Tools: Python, Py Torch/Tensor Flow, scikit‑learn.
• Data & Infra: SQL, Spark, Airflow, cloud platforms (Azure, AWS, GCP).
• Solid understanding of distributed systems, APIs, and cloud infrastructure (Azure, AWS, or GCP).
• Familiarity with reinforcement learning or contextual bandits for adaptive decisioning in dynamic environments.
• Familiarity with graph algorithms and path planning for spatial routing and pick path optimization.
• Skilled in designing and analyzing A/B tests or switchback experiments for operational models.
• Experience in an Agile working environment and at least one related project management tool (Azure Dev Ops, Jira, etc.).
• Comfortable presenting results to cross functional partners and help them understand technical trade offs.
• Brings a collaborative, problem solving and growth mindset to all interactions with a strong focus on delivery.
• Experience with real‑time decisioning systems and streaming data architectures.
• Familiarity with reinforcement learning or hybrid ML‑OR frameworks.
• Background in e Commerce, retail, or customer‑facing fulfillment systems.
• Strong understanding of experimentation design and causal inference.
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