Lead Decision Scientist, Revenue - Ads, Revenue

Over de vacature

Overview

As Staff Decision Scientist in Vinted's Revenue domain, you will own the Ads decision measurement framework and shape how advertisers and the marketplace are evaluated. You'll lead the design of metrics, methodologies, and tradeoff frameworks to scale Ads responsibly across the platform. You'll work closely with cross-functional teams to balance short-term revenue with marketplace health and long-term value. This role offers the opportunity to turn ambiguous questions into measurable decisions and influence senior stakeholders. You'll join a mission-driven team building a world-class advertising ecosystem at scale.

Arbeidsvoorwaarden / Benefits

• share options programme
• 25 days of paid annual leave
• MacBook devices
• EAP for you and family
• team-building events
• home office budget up to 540 EUR for furniture

Verantwoordelijkheden

• Own the Ads decision measurement framework and analytics used to evaluate major Ads decisions across product, pricing, placement, and monetization
• Develop tradeoff frameworks and guardrails balancing revenue, advertiser upside, and marketplace health
• Define evaluation standards beyond standard A/B tests, including incrementality, attribution, quasi-experiments, and sparse data scenarios
• Govern the Ads metric tree ensuring consistent, decision-useful definitions across Product, Data Science, Commercial, and Leadership
• Measure marketplace impact by translating risks into measurable signals affecting buyer/seller experience, discoverability, fairness, and retention
• Partner with Analytics Engineering to define scalable data models aligned with decision needs and business roadmaps
• Align cross-functional strategies by turning ambiguous questions into quantifiable inputs for execution
• Influence senior stakeholders through clear, rigorous data and communication

Belangrijkste vereisten

• Advertising publishing and marketplace knowledge

• Strong analytical and causal inference judgment

• Ability to navigate high ambiguity and structure complex problems

• Experience designing measurement methodologies in imperfect environments (sparse data, misaligned timing, quasi-experiments)

• Strong product and business sense with ability to convert signals into quantitative inputs

• Ability to influence senior cross-functional stakeholders with clarity and rigor

• 高 tolerance for ambiguity

• strong cross-functional collaboration

• clear communication

• causal inference

• measurement methodologies

• incrementality logic

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