Ads AI Analytics Lead II

Instacart is transforming the grocery industry by providing grocery delivery and pickup services. The company operates on a Flex First model, allowing employees to choose where they work—from home, an office, or elsewhere—while staying connected through in-person events.

The Ads AI Analytics Lead II will own the intelligence behind Instacart's Ads agents, designing the semantic and context layer for advertising and building production-grade agents that analyze campaigns, diagnose performance, and recommend actions to improve ROAS, pacing, and partner outcomes. This role collaborates with Ads GTM, Product, Data Science, and Engineering to ship vertical agents with measurable lift.

• Define Ads ontologies and canonical metrics across campaigns, budgets, bids, creatives, audiences, and placements.
• Build robust dbt models and curated marts in Snowflake or BigQuery with data contracts, tests, SLOs, and monitoring; orchestrate pipelines with Airflow.
• Ingest, structure, and enrich unstructured Ads content; publish retrieval-ready datasets using managed search/vector services.
• Design and evaluate RAG workflows (hybrid search, re-ranking) with quality and latency targets.
• Design agent reasoning, tools, and policies for Ads use cases, including human-in-the-loop approvals and guardrails.
• Establish evaluation suites and dashboards tracking precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B and uplift experiments to quantify business impact; own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time-to-insight.
• Partner with Ads Product, Ads Engineering, Sales leadership, Data Engineering, and R&D to align roadmaps and ship production agents.
• Own the workstream end to end—from data and pipelines to UI and agent logic.

• 4–7 years of experience in analytics engineering, data science, or applied AI, with advanced proficiency in Python and SQL.
• 2+ years working with ads, retail, or e-commerce data.
• Hands-on experience with dbt and Snowflake or BigQuery, including data modeling, testing, documentation, and managing data contracts.
• Experience orchestrating data pipelines with Airflow (or similar scheduler), including alerting and on-call support for data SLOs.
• Ability to design and run offline/online evaluations and A/B or uplift tests; familiarity with experiment design and statistical inference.
• Fluency in Ads analytics concepts: ROAS, CPA, CTR, CVR, LTV, pacing, auction dynamics, and incrementality.
• Shipped at least one production data or AI system used by business stakeholders.
• Experience with evaluation and guardrail frameworks and human-in-the-loop QA workflows.
• Proficiency with at least one BI/visualization tool (e.g., Looker, Tableau, Mode, or Power BI).
• Bachelor’s degree in a quantitative field or equivalent practical experience.

• Experience building AI-driven products or agents end to end, including retrieval design and vector search.
• Deep expertise in advertising products and operations.
• Applied experience with Ads modeling techniques such as forecasting, anomaly detection, uplift modeling, and causal inference.
• Hands-on experience with workflow automation or internal tooling (e.g., Retool, Superblocks, Zapier, n8n, Gumloop) and/or front-end frameworks (e.g., React).
• Familiarity with retail media and ad ecosystems (e.g., Amazon Ads, Google Ads, Meta, Shopify, DoorDash).

This role is remote and eligible for a new hire equity grant as well as annual refresh grants. For Canadian based candidates, the base pay range is CAN$140,000—$148,000 CAD. Offers may vary based on experience and skills.

Application Instructions: Apply via the provided link.