Lead Decision Scientist, Revenue - Ads, Revenue (Vinted, UAB)

Job description:
Vinted’s Revenue domain is scaling fast, and our Ads business is on a mission to develop a world-class advertising ecosystem within Vinted. We are looking for a Lead Decision Scientist to act as a strategic technical partner and crucial counterpart to our Data Science Lead.
While our Data Scientists focus on optimization systems (algorithms, ranking, bidding), this senior individual contributor role is entirely focused on measurement, evaluation, and decision frameworks. You will be responsible for identifying and resolving systemic data problems, ensuring that Advertising products are evaluated consistently across advertiser performance, business impact, and overall marketplace health. You will bring structure to ambiguous decisions by defining the metrics, methodologies, and tradeoff frameworks needed to scale Ads responsibly across Vinted’s entire real estate.
Own the Ads decision measurement framework. Create the analytical frameworks used to evaluate major Ads decisions across product, pricing, placement, and monetization, making risks, assumptions, and tradeoffs explicit.
Develop tradeoff frameworks and guardrails. Create the standard framework to weigh short-term ads revenue and advertiser upside against marketplace health (transactions, listings, retention). You will "price the real estate" across the app (homepage, search, messaging, etc.) for different user segments and journeys.
Define measurement and evaluation methodology. Establish evaluation standards where standard A/B testing isn't enough, including incrementality logic, attribution approaches, quasi-experimental methods, and practical frameworks for sparse data or delayed-measurement scenarios.
Govern the Ads metric tree. Ensure advertiser, business, and marketplace metrics are clearly defined, consistently interpreted, decision-useful, and aligned across Product, Data Science, Commercial, and Leadership teams.
Measure marketplace impact. Translate abstract marketplace risks into measurable signals. Establish practical ways to assess how Ads affect buyer/seller experience, organic discoverability, fairness, and long-term member retention.
Partner on data and measurement readiness. Work closely with Analytics Engineering to define scalable, future-ready data models that align with decision-making needs. Ensure long-term data needs are aligned with business roadmaps.
Align cross-functional strategies. Turn ambiguous questions from Product, AdOps, Commercial, and Strategy teams into quantifiable inputs and analytical frameworks that guide execution and surface methodological risks early.
Requirements:
Advertising publishing and marketplace knowledge.
Strong analytical and causal inference judgment.
High ambiguity tolerance and the ability to bring structure to complex problems.
Experience designing measurement methodologies in imperfect environments (e.g., sparse data, misaligned-in-time measurements, quasi-experiments).
Strong product and business sense, with the…