Group Product Manager - Data Science

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:

We are looking for a strategic, user-obsessed Group Product Manager to lead DICK’S Sporting Goods’ Data Science portfolio. This leader will own the vision, strategy, and execution for data science products that shape experiences and drive measurable business impact across key business priorities.

This role will define the vision for end-to-end Data Science and Machine Learning capabilities, translating business needs into technical delivery across Data Science, ML Engineering, and MLOps. Working closely with Domain and Solution Architects, this leader will drive innovation, automation, and scalable execution across the portfolio. They will also serve as a strategic partner to business and technology leaders, with clear accountability for turning model output into production impact and sustained business value.

This person will lead and coach a team of senior Product Managers and act as a connective leader across Data Science, Engineering, Analytics, Design, and business teams. They must bring strong product judgment, organizational leadership, and technical fluency, with the ability to translate complex machine learning capabilities into clear direction, scalable operating models, and tangible outcomes for users and the business.

• Define and lead the multi-year vision, strategy, and roadmap for data science products and capabilities.

• Lead and develop a team of senior Product Managers, setting a high bar for strategic thinking, product rigor, prioritization, and execution excellence.

• Partner closely with Data Science, ML Engineering, Software Engineering, Analytics, UX, and business teams to translate user and business needs into scalable product solutions.

• Lead the end-to-end lifecycle of data science products, from opportunity sizing and problem definition through experimentation, launch, activation, adoption, and optimization, ensuring model outputs drive measurable business impact.

• Partner with Data Science, ML Engineering, and Data Engineering to productionize, scale, deploy, and monitor machine learning models within reliable production pipelines.

• Establish operating alignment across Data Science teams and platforms to enable consistent model outputs, reusable capabilities, and scalable activation across the portfolio.

• Advance activation and decisioning capabilities through data science, enabling more precise targeting, personalization, and optimization across channels.

• Ensure the portfolio delivers measurable value through clear success metrics tied to user experience, engagement, conversion, revenue, efficiency, and ROI.

• Ensure the portfolio delivers measurable value through clear success metrics tied to forecast accuracy, inventory efficiency, margin improvement, pricing effectiveness, service levels, and operational ROI.

• Establish a cohesive portfolio strategy across model-driven experiences, decisioning systems, experimentation frameworks, and activation channels.

• Translate complex machine learning concepts into clear, executive-ready narratives, priorities, and tradeoff decisions for senior stakeholders.

• Create strong operating mechanisms across teams, including roadmap reviews, backlog prioritization, dependency management, release planning, and alignment to data and technology platform evolution.

• Deliver data science products against defined OKRs, with clear accountability for impact, timelines, and efficient use of resources.

• Build strong relationships with business and technology leaders to align on strategy, priorities, and delivery.

• Influence and develop talent across Data Science, ML Engineering, and Analytics by setting a high bar for product thinking, experimentation rigor, and outcome-driven delivery.

• Champion a disciplined product management approach to data science, including clear problem framing, hypothesis-driven development, experimentation, and post-launch learning.

• Partner with legal, privacy, and governance teams to ensure responsible use of data and compliant deployment of data science capabilities.

Candidates may bring relevant experience from either customer-facing digital domains or supply chain, merchandising, and operations-focused domains.

Customer-Facing Digital Experience

• Experience in domains such as eCommerce, Stores, mobile app, search, recommendations, personalization, marketing technology, advertising technology, media, or related customer-facing digital experiences.

• Familiarity with capabilities such as ranking systems, recommendation systems, audience strategy, decisioning platforms, attribution, campaign optimization, or media effectiveness.

• Experience applying data science and machine learning to customer experiences, targeting, personalization, discovery, or optimization problems at scale.

Supply Chain, Merchandising, and Operations Experience

• Experience in domains such as supply chain, merchandising, demand forecasting, pricing, or optimization.

• Familiarity with capabilities such as demand forecasting, inventory optimization, assortment planning, replenishment, pricing optimization, allocation, or markdown optimization.

• Experience applying data science and machine learning to forecasting, inventory, pricing, assortment, supply chain, or operational optimization problems at scale.

QUALIFICATIONS:

• 12 – 15 years of experience in data science, product, or related domains, preferably in data science product management, digital strategy, or technical product leadership.

• Product management experience with a strong track record of leading complex, data-driven, or machine learning-powered products.

• Demonstrated ability to lead cross-functional teams spanning Data Science, Engineering, Design, Analytics, and business stakeholders.

• Deep understanding of experimentation, KPI design, measurement frameworks, and how to evaluate model-driven product performance in production.

• Strong technical fluency in machine learning concepts, data products, decisioning systems, and applied AI/ML workflows, with the ability to partner effectively with technical teams without needing to be the hands-on builder.

• Track record of defining strategy, building roadmaps, and delivering measurable business outcomes in fast-paced, highly matrixed environments.

• Excellent written and verbal communication skills, with the ability to influence executive stakeholders and translate complexity into clear, business-oriented decisions.

• Bachelor's degree in a relevant field required; advanced degree in business, computer science, engineering, analytics, data science, or a related discipline preferred.

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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!