At DICK’S Sporting Goods, we believe in how positively sports can change lives. On our team, everyone plays a critical role in creating confidence and excitement by personally equipping all athletes to achieve their dreams. We are committed to creating an inclusive and diverse workforce, reflecting the communities we serve.
If you are ready to make a difference as part of the world’s greatest sports team, apply to join our team today!
OVERVIEW:
Are you a systems-oriented data scientist with deep expertise in AI, Machine Learning, and Data Science? Do you thrive on building decision engines that drive real-time personalization, identity resolution, and customer intent modeling? Are you ready to shape the future of a $12B+ sports retailer with 800+ physical stores and a rapidly growing digital footprint? Join a high-performing team of ML Engineers and Scientists to co-create enterprise-grade AI systems that deliver measurable impact across search, fulfillment, and customer experience.
We are seeking a Senior Data Scientist (Customer Intent, Decision Engine) to lead the development of intelligent decisioning systems that model customer intent and dynamically resolve identity in real time. This role sits at the intersection of machine learning, causal inference, and enterprise-scale systems — powering decisions that determine which customer identity to associate in the moment, and what action best serves their needs. You’ll design hybrid models that combine behavioral prediction with causal reasoning, enabling personalized experiences across search, fulfillment, and digital commerce.
This role requires a subject matter expert with deep experience in traditional machine learning and cutting edge AI with a strong foundation in causal inference. You’ll apply advanced modeling techniques to design intelligent decisioning systems that dynamically resolve customer identity and model real-time intent — enabling personalized experiences across search, fulfillment, and service.
Your work will focus on solving complex decision problems — from identity association and behavioral prediction to session stitching and service layer optimization — using a blend of predictive analytics, causal modeling, simulation, and mathematical programming. You’ll build hybrid models that combine behavioral signals with counterfactual reasoning, powering decisions that determine which customer identity to associate and what action best serves their needs.
As a Senior Data Scientist, you’ll influence the enterprise decisioning landscape by developing models that integrate with high-impact systems across backend data platforms and orchestration layers. You’ll collaborate with product, engineering, and business leaders to translate ambiguous customer journeys into solvable data science problems, and help them understand the art of the possible through rigorous experimentation, simulation, and model design. Your work will directly influence personalization quality, identity precision, and service responsiveness — ensuring every customer interaction is context-aware, relevant, and frictionless.
Decision Engine Architecture
Design and implement large-scale decision engines that dynamically resolve customer identity and intent in real time.
Develop optimization frameworks (multi-armed bandits, reinforcement learning, constrained optimization) to balance personalization, business KPIs, and operational constraints.
Real-Time Intent Prediction
Build session-based and sequence-aware models (transformers, RNNs, temporal point processes) to capture evolving customer intent.
Incorporate multimodal signals (clickstream, search queries, transaction history, CRM attributes) into unified intent prediction pipelines.
Deploy models with low-latency inference for real-time decisioning at scale.
Evaluation & Experimentation
Define rigorous evaluation metrics (precision/recall for identity resolution, uplift for causal models, latency for decision engines).
Lead online experimentation (A/B tests, multi-cell experiments) to validate model impact on personalization, conversion, and customer experience.
Apply counterfactual evaluation techniques to measure causal impact of identity decisions.
Cross-Functional Collaboration & Communication
Translate technical insights into clear recommendations for product, marketing, and executive stakeholders.
Partner with product managers to align decision engine outputs with business objectives (e.g., personalization, fraud prevention, customer support).
Communicate model performance to technical and non-technical audience.
Data Engineering & Infrastructure
Ensure models are production-ready with robust monitoring, retraining schedules, and A/B testing frameworks.
Implement feature stores and model registries to streamline experimentation and deployment.
Advanced degree (MS/PhD) in Computer Science, Statistics, Applied Mathema0cs or related field.
4+ years of experience in data science, machine learning or AI with a track record delivering production systems.
Strong proficiency in hybrid modeling techniques.
Machine Learning: e.g. deep learning, sequence models and transformers, recommender systems.
Simulation and Mathematical Programming: e.g. MIP, agent simulation, LP etc.
Causal Inference: e.g. Treatment effect estimation, counterfactual reasoning etc.
Experience building real-time recommendation systems that adapt to evolving customer behavior and context, using session-based modeling, embeddings, and reinforcement learning.
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 enterprise orchestration layers, integrating models into backend systems that power search, fulfillment, customer support, and personalization.
Skilled in designing and analyzing A/B tests.
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.
Prior work in eCommerce, retail, or customer identity resolution.
Experience with synthetic data generation or simulation frameworks for intent modeling.
Knowledge of optimization modeling (decision trees, bandits, reinforcement learning for personalization).
QUALIFICATIONS:
Bachelor's Degree or equivalent level preferred
General Experience: Experience enables job holder to deal with the majority of situations and to advise others (Over 3 years to 6 years)
Managerial Experience: Basic experience of coordinating the work of others (4 to 6 months)
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VIRTUAL REQUIREMENTS:
At DICK’S, we thrive on innovation and authenticity. That said, to protect the integrity and security of our hiring process, we ask that candidates do not use AI tools (like ChatGPT or others) during interviews or assessments.
To ensure a smooth and secure experience, please note the following:
Cameras must be on during all virtual interviews.
AI tools are not permitted to be used by the candidate during any part of the interview process.
Offers are contingent upon a satisfactory background check which may include ID verification.
If you have any questions or need accommodations, we’re here to help. Thanks for helping us keep the process fair and secure for everyone!
Targeted Pay Range: $83,000.00 - $138,200.00. This is part of a competitive total rewards package that could include other components such as: incentive, equity and benefits. Individual pay is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all teammate pay regularly to ensure competitive and equitable pay.DICK'S Sporting Goods complies with all state paid leave requirements. We also offer a generous suite of benefits. To learn more, visit www.benefityourliferesources.com.