Data Science Manager, Shopping Experience

• Lead, mentor, and grow a high-performing team of data scientists; set clear priorities, uphold technical excellence, and develop career paths.
• Define and own the analytics and experimentation strategy across storefront, browse/aisles, search, cart, checkout, OSP, Family, Lists, and Meals/Health-covering metrics, guardrails, instrumentation, and experiment best practices.
• Own core shopping metrics and event logging; improve data quality, build reusable dashboards/tools, and ensure reliable, timely insights for decision-making.
• Drive “DS understand projects” that uncover friction in shopping funnels; scope root-cause analyses and partner with PM and Eng to prioritize and ship fixes that move conversion and retention.
• Partner as a thought leader with Product and Engineering leadership to shape roadmaps, make tradeoffs across Enterprise, Lifecycle, Category Growth, and Foundations work, and ensure goals are measurable and achievable.
• Set the bar for experiment design and readouts; coach teams on hypothesis formation, sampling, power analysis, metric selection, and clear storytelling of results and implications.
• Collaborate with ML partners on ranking, recommendations, and personalization initiatives, aligning offline/online evaluation with business outcomes and shopper experience goals.
• Influence and improve cross-functional rituals (e.g., experiment reviews, prioritization forums) to increase speed, rigor, and learning across the organization.
• Ensure AI/agentic features are grounded in robust data and measurement frameworks, with clear definitions of success and long-term impact.

• 6+ years of experience in data science or analytics, including 2+ years directly managing or tech-leading a data science team in a fast-moving environment.
• Proven track record optimizing consumer app experiences at scale (e.g., funnels, ranking, personalization, experimentation) in technology or similar product-led organizations.
• Deep hands-on expertise in experimentation and measurement, including A/B testing, guardrails, metrics design, and logging/event instrumentation.
• Strong analytical and technical skills with proficiency in SQL and Python or R.
• Experience translating ambiguous customer and retailer problems into clear metrics, analyses, and experiment roadmaps that drive measurable outcomes.
• Demonstrated cross-functional leadership with Product, Engineering, Design, and Operations; able to influence roadmaps and drive alignment without formal authority.
• Track record as a data quality champion-improving instrumentation, building reusable dashboards/tools, and advocating for rigorous yet pragmatic decisions.
• Excellent communication and storytelling skills; able to explain complex tradeoffs and experiment results to non-technical partners and senior stakeholders.

• Experience in e-commerce, marketplaces, or consumer mobile apps, especially in grocery/retail or multi-retailer environments.
• Background in ranking, recommendation, or personalization systems (e.g., search, recommendations, or personalization platforms) and their evaluation.
• Prior experience standing up or scaling experimentation programs, including tiering, experiment review forums, guardrails, or governance.
• Experience leading or partnering closely with ML teams on production models and offline/online evaluation strategies.
• Success operating in a highly cross-functional, matrixed organization and influencing director+ and VP+ stakeholders.
• Experience defining metric frameworks for complex, multi-surface journeys (e.g., friction metrics, “bad shopping paths,” long-term outcomes).
• Familiarity with GenAI and AI-assisted workflows (e.g., using LLMs for analytics, experimentation tooling, or product features).
• Track record of building data science culture through mentoring, best practices, and process improvements.

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