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.

4–7 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.

Ads AI Analytics Lead

Key skills