Director of Applied AI/ML Science - Ads

<div class="content-intro"><p><span style="font-weight: 400;"><strong>About Faire</strong></span></p> <p>Faire is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At Faire, we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.</p> <p>We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.</p></div><p><strong>About this Role</strong></p> <p>Ads is one of the fastest-growing and most strategically important parts of Faire's business, and the Ads Data team — now three years old — is entering a period of hypergrowth. As Director of Data for Ads, you will own the end-to-end data vision and strategy for the Ads business — from the applied science and ML powering our marketplace to the data foundation underneath it.</p> <p>You'll lead and grow a team spanning Applied Scientists and Analytics/Data Engineers, set the technical vision and roadmap for the group, and partner closely with cross-functional leaders in Product, Engineering, Design, and Strategy &amp; Analytics (S&amp;A) to define how the Ads org operates — its planning cadence, prioritization framework, and execution rigor. This is a rare opportunity to shape, from an early stage, the technical and organizational backbone of one of Faire's most important growth engines.</p> <p><strong>What You'll Do</strong></p> <ul> <li>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).</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>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).</li> <li>Represent Ads Data in company-level strategic conversations, ensuring data and ML considerations are embedded early in product and business planning.</li> </ul> <p><strong>Qualifications</strong></p> <ul> <li>8+ years of experience in data science, applied ML, or ML engineering roles, ideally with exposure to ads, search, recommendation systems, marketplaces, or auctions.</li> <li>4+ years of experience managing technical teams of 5+ people, including senior ICs.</li> <li>Direct experience with ads marketplaces, auction systems, or search/recommendation systems.</li> <li>Demonstrated ability to set technical vision and strategy for a team or org, and to translate that strategy into roadmaps, priorities, and measurable outcomes.</li> <li>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.</li> <li>Strong track record partnering with cross-functional partners to define team operating models and drive cross-functional execution.</li> <li>Exc