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
Instacart's Ads Optimization team is the decision-making engine of our $1B+ ads business. We own the systems responsible for bidding, pacing, budgeting, targeting, and additional optimization layers that convert advertiser goals into real-time outcomes — all while balancing value across users, advertisers, retail partners, and Instacart. As a Senior Data Scientist on this team, you will drive the analytics, experimentation, and algorithmic strategy that determines how our ad supply and demand meet, and how value and surplus are created and distributed across the platform.
In this role, you will operate at the intersection of data science, economics, and production systems, partnering closely with Product, Engineering, and Machine Learning to build and ship solutions that directly impact Instacart's revenue and advertiser outcomes. Your work will touch the full research-to-production loop — from problem framing and modeling to experimentation, measurement, and iteration in live systems.
About the Job
- Own the analytics and experimentation strategy for pacing, targeting, and related optimization areas, driving measurable improvements in advertiser outcomes and platform efficiency
- Design and build intelligent pacing algorithms and budget allocation systems grounded in adaptive control and model-predictive control techniques, deployed in high-throughput production environments
- Lead end-to-end experimentation across optimization systems — designing experiment frameworks, diagnosing complex or conflicting signals, and translating results into clear, actionable recommendations for Product and Engineering leadership
- Apply causal inference, statistical modeling, and ML techniques to optimize how ad spend is distributed across time and auction opportunities, balancing user experience, advertiser goals, and platform revenue
- Present findings and strategic recommendations to leadership across Product, Engineering, and Data Science
- Serve as a thought leader and technical anchor in the Ads Optimization space, setting the analytical standard and mentoring peers across the team
- Collaborate with the Ads Product and Sales teams to gather advertiser feedback and inform optimization priorities
About You
Minimum Qualifications
- Proven experience in ads optimization at an ad tech company (e.g., Google, Meta, Amazon, TikTok, or equivalent), with hands-on work in pacing, targeting, budget allocation, or related systems
- Strong data science foundation: product and data analysis, A/B experimentation, causal inference, and statistical modeling
- Experience shipping optimization systems into production — not just research, but real measurement and iteration in live environments
- Excellent communication skills, with the ability to synthesize complex technical findings for senior business and engineering stakeholders
- 6+ years of work experience in a data science or related field
Preferred Qualifications
- Experience collaborating directly with an ads product or sales team, or with advertisers, to shape product and algorithmic priorities
- Background in budget-constrained allocation methods, adaptive control, or model-predictive control in production systems
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