Staff Product Manager, Buyer ML

Etsy•Brooklyn, NY

3d•Hybrid

About The Position

At Etsy, we’re building the future of meaningful commerce. We connect buyers and sellers around the world, helping people discover items that feel personal, significant, and made with care—while supporting millions of independent makers along the way. With over 100 million unique items, Etsy is a place where buyers can form real connections through commerce and find things they truly can’t get anywhere else! The Buyer Experience product group is responsible for how buyers discover, connect with, and purchase our sellers' unique items. In 2026, we're working toward a future where Etsy becomes a creative destination where buyers explore ideas, feel personally understood, connect with real makers, and return frequently, not because they have to, but because they’re inspired to. We are looking for a Staff Product Manager to join our XBuyer ML team. In this role, you'll be a key product leader in how Etsy uses machine learning to connect buyers with the right items at the right moment, working across recommendations, personalization, and retrieval systems that serve millions of buyers every day. This is a deeply collaborative, matrixed role: your engineering partners sit within the Matching, Relevance, and Recommendations group, and your product work directly serves multiple teams across the Buyer Experience Group. You'll work in close partnership with Buyer Signal Capture teams to ensure the signals powering our ML systems reflect real buyer intent, and alongside peer PMs across those groups to align on strategy and sequencing. Success in this role requires someone who can lead through influence, not just authority. You'll need to earn credibility with engineering and science partners, build genuine alignment across consuming product teams, and bring a rigorous point of view on where ML investment creates the most incremental buyer value, even when you don't control all the inputs. Because of the cross-group scope and the prioritization complexity that comes with it. This role reports directly to the VP of Buyer Experience, ensuring the work is resourced and sequenced for maximum impact.

Requirements

6+ years of product management experience , with a meaningful portion spent contributing to ML-powered products in recommendations, search, personalization, or similar domains at a technology company.
Deep ML product fluency : you understand how retrieval systems, ranking models, embeddings, and recommendation pipelines work well enough to have credible technical conversations with engineers and scientists, and to translate model tradeoffs into product decisions.
Strong analytical mindset : you can break ambiguous buyer problems into sized opportunities using data, define the right metrics (including the limits of those metrics), and construct a counterfactual case for where ML investment creates the most incremental value.
Proven ability to operate in a matrixed environment : you have experience leading product work across engineering and product partners who don't share the same org structure, earning influence through credibility and clear prioritization rather than formal authority.
Proven discovery discipline : you have a track record of attacking the biggest risk first before scaling investment, using lightweight tests, mining existing signals, and letting evidence drive Stop/Pivot/Proceed calls rather than momentum.
Experimentation fluency : strong foundation in A/B testing including hypothesis formulation, metric selection, results interpretation, and knowing when an experiment can and cannot answer your question.
Cross-functional leadership : you've successfully driven alignment across ML engineering, applied science, analytics, and research, and earned engineering credibility through domain depth and clear decision-making, not just process.
Initiative-level scope : experience operating in ambiguous spaces where you've contributed to originating strategy across multiple teams or consuming partners, not just executed a defined roadmap.
A genuine commitment to learning and growth : ML is moving fast. You stay current, share what you're learning, and give and receive direct feedback as a matter of habit.

Responsibilities

Partner with peer PMs and senior stakeholders to shape and contribute to the ML product strategy for buyer discovery, bringing a clear point of view on where investment creates the most incremental value and helping sequence the work across teams.
Navigate a matrixed structure with fluency: partner closely with engineering teams in Matching, Relevance, and Recommendations while keeping Buyer Experience teams aligned on priorities and outcomes.
Work in close partnership to define what signals matter, ensure those signals are captured well, and translate buyer behavior data into ML opportunities that improve discovery at scale.
Set the discovery approach for your workstreams: name the biggest risk to each bet, go after it first with the cheapest tool that will teach you something, and invalidate hypotheses before committing significant engineering investment.
Partner deeply with ML engineers and applied scientists to prioritize, build, evaluate, and launch ML-driven solutions, from retrieval and ranking to personalization and recommendations, that address real buyer needs and drive measurable business impact.
Define and track outcome metrics that go beyond model quality to the buyer behavior and business outcomes that demonstrate incremental value.
Drive prioritization conversations that balance the needs of multiple consuming teams, making the tradeoffs visible to the VP and senior stakeholders when alignment is needed.
Analyze product performance, synthesize signals from analytics, research, and customer feedback, and use that evidence to sharpen prioritization, including making the case to invest more, or to stop.