Ads AI Analytics Lead II

About the Job

• 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.

About You

Minimum Qualifications

• 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.

Preferred Qualifications

• 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.