Senior Data Scientist – Customer Intent, Decision Engine

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DICK'S Sporting Goods

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Retail • Sports • eCommerce

DICK'S Sporting Goods is the largest sporting goods retailer in the United States, offering a wide range of sporting equipment, apparel, and footwear. Committed to enriching communities through sports, DICK'S Sporting Goods also partners with major sports brands like UnderArmour, Patagonia, and Nike to provide high-quality products. The company is dedicated to inclusivity and sustainability, providing resources and support to help athletes and children participate in sports. Headquartered in Pittsburgh, DICK'S is at the forefront of innovative retail and fosters a people-first culture. The company also runs the DICK'S Sporting Goods Foundation, emphasizing the transformative power of sports.

10,000+ employees

Founded 1948

🛒 Retail

⚽ Sports

🛍️ eCommerce

📋 Description

• 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. • 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. • 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. • 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). • 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.

🎯 Requirements

• Advanced degree (MS/PhD) in Computer Science, Statistics, Applied Mathematics 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).

🏖️ Benefits

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Professional development opportunities