Responsibilities

• Define the holistic data vision and strategy for Ads, spanning retrieval/ranking ML (search ads relevance, query understanding, personalization), bidding/marketplace/auction systems (auction design, bid optimization, pacing, budget allocation, advertiser ROI), and data engineering/ETL (pipelines, data foundations, and analytics that power decision-making across the org).
• People-manage and grow the full group of Applied Scientists and Analytics Engineers on the Ads Data team — hiring, mentoring, setting career development paths, and building a strong technical culture as the team scales through hypergrowth.
• Own the team's long-term technical roadmap, ensuring it's tightly aligned to company and Ads org strategy, and translate that roadmap into clear priorities, staffing plans, and execution.
• Partner with cross-functional leaders to design and run the team's operating model — planning cadence, prioritization frameworks, roadmap reviews, and cross-team rituals — so Ads Data operates as a high-leverage, well-run function as it grows.
• Drive significant business impact through hands-on contribution to high-priority ML/algorithmic problems when needed, while building the team's ability to deliver this work independently and at scale.
• Serve as a key member of the broader Data leadership team, contributing to how the data org is built and run — both technically (architecture, tooling, standards) and organizationally (processes, career frameworks, hiring bar).
• Represent Ads Data in company-level strategic conversations, ensuring data and ML considerations are embedded early in product and business planning.

Requirements

• 8+ years of experience in data science, applied ML, or ML engineering roles, ideally with exposure to ads, search, recommendation systems, marketplaces, or auctions.
• 4+ years of experience managing technical teams of 5+ people, including senior ICs.
• Direct experience with ads marketplaces, auction systems, or search/recommendation systems.
• Demonstrated ability to set technical vision and strategy for a team or org, and to translate that strategy into roadmaps, priorities, and measurable outcomes.
• Comfort operating across a broad technical surface area — from ML modeling (bidding, auction, ranking, relevance) to data engineering and analytics infrastructure — with enough depth to earn credibility with ICs across all of these areas.
• Strong track record partnering with cross-functional partners to define team operating models and drive cross-functional execution.
• Excellent communication skills, with the ability to flex between technical depth and business-level narrative depending on the audience.
• Comfort with ambiguity and rapid change — this team is young and growing fast, and the role requires building process and structure while the ground is still shifting.

Preferred

• Academic background in Computer Science, Machine Learning, Statistics, Math, Operations Research, or a related field; PhD a plus.
• Prior experience building or scaling a data/applied science org from an early stage.