Data Science Manager, Shopping Experience

Confirmed live in the last 24 hours

Instacart

United States - Remote

Remote

Posted April 1, 2026

Job Description

We're transforming the grocery industry

At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.

Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.

Instacart is a Flex First team

There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.

Overview

Instacarts Shopping Experience team, part of the Shopping org within Core Experience, owns the end-to-end consumer journey with a single retailer—from storefront and browse, through search and item discovery, into cart and checkout, and all the way to the order status page. We’re focused on building intuitive, data-informed experiences that help customers find what they love, faster.

We’re looking for a Data Science Manager to lead a team of data scientists and shape the analytics and experimentation strategy across critical shopper surfaces including storefront, browse/aisles, search, cart, checkout, OSP, Family, Lists, and Meals/Health. In this highly cross-functional role, you’ll partner closely with Product, Engineering, Design, and Operations to define the right metrics, raise the bar on experiment design and readouts, and deliver insights that drive higher conversion, retention, and satisfaction—while fueling stronger outcomes for retailers and data-grounded AI features.

This is a fast-paced environment with evolving priorities and complex tradeoffs across Enterprise, Lifecycle, Category Growth, and Foundations work. You’ll thrive here if you love rolling up your sleeves, operating with clarity amid ambiguity, and bringing crisp measurement and storytelling to high-impact decisions at scale.

About the Job

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.