Senior Data Scientist I - Meals

Overview

We are looking for a Senior Data Scientist to lead the Meals initiative — one of Instacart's top strategic priorities for 2027. This role is focused on scaling data science work following the MVP launch, with metric tracking and experimentation at the core. You will play a key role in driving engagement, activations, and reactivations for Instacart through a high-visibility, high-impact 0→1 initiative. This is an opportunity to shape the direction of a major product area in close partnership with Product, Engineering, and Design.

About the Job

• Lead data science for the Meals initiative, owning the post-MVP scale-up of DS systems and infrastructure
• Design, build, and maintain metric tracking frameworks to measure the impact of Meals on engagement, activation, and reactivation
• Drive experimentation strategy — from hypothesis definition to analysis and recommendation — to guide product decisions
• Partner closely with Product Managers, Engineers, and Designers to align on strategy and communicate data-driven insights
• Contribute to a 0→1 initiative at a critical inflection point, helping define what success looks like at scale

About You

Minimum Qualifications

• Proven experience in consumer-facing product data science, partnering closely with Product Managers, Engineering Leads, and Data Engineers
• Strong data science foundation: product and data analysis, A/B experimentation, causal inference, and statistical modeling
• Experience defining KPIs, and optimizing funnel for product market fit
• Excellent communication skills, with the ability to synthesize complex technical findings for senior business and engineering stakeholders
• 4+ years of work experience in a data science or related field
• Bachelor’s degree in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Economics, Engineering) or equivalent practical experience

Preferred Qualifications

• Experience bridging offline evaluation metrics (e.g., NDCG, precision/recall, human evaluation) with online experiments and business outcomes.
• Background in causal inference beyond standard A/B tests (e.g., holdouts, diff-in-diff, quasi-experiments) to measure long-term or cross-surface effects.
• Proven impact improving logging, instrumentation, and metric definitions in complex data environments.
• Experience launching new features in new markets, or launching 0-1 initiatives