Ads AI Analytics Lead II

About the job

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.

Responsibilities

• 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.
• 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.

Requirements

• 3–6 years in analytics engineering, data science, or applied AI with strong SQL and Python.
• Advanced proficiency in Python and SQL, with experience using dbt and Snowflake or BigQuery, including skills in data modeling, testing, and managing data contracts.
• Experience with at least one data visualization tool (Tableau, Mode, Power BI, Looker, or similar).
• 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.

Preferred Qualifications

• Strong understanding of AI and machine learning concepts, with experience creating AI-driven products.
• 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.