The Commercial Scaled Intelligence (CSI) team is an AI-first team dedicated to delivering actionable commercial insights and scalable automation to drive revenue growth and operational efficiency across the company. The team focuses on intelligence generation, predictive analytics, and workflow automation to enable data-driven decision-making and optimize commercial performance.
As an Ads AI Analytics Lead II, you will own the intelligence behind our Ads agents. You will design the Ads semantic/context layer and build vertical AI agents that analyze campaigns, diagnose performance, and recommend actions that improve ROAS, pacing, and partner outcomes. You will partner with Ads GTM, Product, Data Science, and Engineering to ship production agents with measurable lift.
- Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
- Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
- Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
- Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
- Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
- Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
- Run A/B or uplift experiments to quantify impact and guide iteration.
- Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.
- 3–6 years in analytics engineering, data science, or applied AI with strong SQL and Python.
- 2+ years of domain expertise in ads, retail, or e-commerce data.
- Advanced Proficiency in Python and SQL, with experience using dbt and Snowflake or BigQuery, including skills in data modeling, testing, and managing data contracts.
- Deep Expertise in orchestrating data pipelines using dbt and Airflow
- Experience with at least one data visualization tool (Tableau, Mode, Power BI, Looker, or similar)
- Ability to design offline/online evaluations and run A/B or uplift tests
- Fluency in Ads analytics concepts such as ROAS, CPA, CTR, CVR, LTV, pacing, auction dynamics, and incrementality.
- Strong stakeholder communication with a track record of shipping production data or AI systems that drove business impact.
- Understanding of ML models to drive recommendations on bid, keywords, and budgets
- Experience with evaluation and guardrail frameworks and human‑in‑the‑loop QA.
- Strong understanding of AI and machine learning concepts, with experience creating AI-driven products.
- Deep expertise in advertising products, including leading and driving automation projects.
- Proven ability to improve operational efficiency through automation initiatives in fast-paced environments.
- Applied experience in modeling techniques for Ads, including forecasting, anomaly detection, uplift modeling, and causal inference.
- Hands-on experience with workflow automation and low-code development platforms (Zapier, n8n, Gumloop, Superblocks)
- Familiarity with retail media or ad platforms, including Amazon, Google, Meta, Shopify, or DoorDash.