Ads AI Analytics Lead II
Own Ads AI agents: design semantic/context layers, build retrieval-ready datasets, and ship production agents that analyze campaigns and recommend actions to improve ROAS and pacing.
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.
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