Senior Data Scientist - Operations Research, Decision Engine (REMOTE)

DICK'S Sporting Goods is committed to creating an inclusive and diverse workforce that reflects the communities they serve. They are seeking a Senior Data Scientist to lead the development of intelligent decisioning systems that optimize fulfillment operations and enhance customer experiences, focusing on machine learning and operations research.

Responsibilities:

• Develop OR-based models for fulfillment routing, labor scheduling, and queuing, triaging & agent 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
• Use graph-based models and reinforcement learning to dynamically adjust pick paths and task sequences
• 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 fulfillment and athlete service solution

Requirements:

• Experience in Operations Research, AI, Machine Learning and Data Science
• Deep experience in traditional machine learning and cutting edge AI with a strong foundation in operations research
• Ability to apply advanced modeling techniques to design intelligent decisioning systems that optimize fulfillment operations
• Experience solving complex optimization problems — from order routing and labor planning to queuing and service layer optimization
• Ability to develop OR-based models for fulfillment routing, labor scheduling, and queuing, triaging & agent optimization
• Experience applying techniques such as mixed-integer programming, dynamic programming, graph theory, spatial optimization and simulation to solve real-time decisioning problems
• Ability to integrate predictive ML models with optimization logic to enable adaptive, data-driven decisions
• Experience building and operationalizing decision engines that automate fulfillment decisions across the enterprise
• Ability to collaborate with engineering to deploy models into production systems with real-time data pipelines and monitoring
• Ensuring models are interpretable, auditable, and aligned with business constraints
• Combining ML outputs with OR solvers via hybrid decision frameworks, enabling scenario-aware optimization and policy simulation
• Ensuring robustness and scalability of models by leveraging containerized environments and observability tools
• Enabling real time decisioning by building & incorporating streaming pipelines and supporting low latency inference and optimization
• Using graph-based models and reinforcement learning to dynamically adjust pick paths and task sequences
• Partnering with product and operations to define decision boundaries, constraints, and success metrics
• Communicating insights and model performance to technical and nontechnical audiences
• Understanding latest research in the field of OR and AI to give inputs to enterprise roadmaps
• Advanced degree (MS/PhD) in Operations Research, Computer Science, Statistics, or related field
• 4+ years of experience in building optimization and ML models in fulfillment, logistics, or supply chain domains
• OR Techniques: linear/mixed-integer programming, simulation, queuing theory
• ML Tools: Python, PyTorch/TensorFlow, 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 DevOps, 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 eCommerce, retail, or customer-facing fulfillment systems
• Strong understanding of experimentation design and causal inference