Lead Decision Scientist, Security (Vinted, UAB)

Job description:
At Vinted, the Security domain protects our marketplace across critical member touchpoints: Registration, Login, User Verification, Bot Prevention, and Authentication & Authorization. Every single member interacts with Security systems before making their first transaction.
As a Lead Decision Scientist in Security, you will serve as the senior technical lead responsible for bringing structure, clarity, and analytical rigor to some of our most critical trade-off decisions. While our Engineering and Data Science teams build defensive infrastructure and detection models, your focus will be entirely on measurement, evaluation, and decision frameworks.
In Security, every action comes with a trade-off: stopping malicious actors earlier prevents harm, but intervening too early or aggressively can frustrate or block legitimate members. Today, we have multiple restriction tools available—from verification challenges to account bans—and we need a clear framework to decide when and how to apply each one.
You will act as an autonomous strategic partner to Security leadership. You will own the analytical foundation for how we restrict malicious activity, quantify user friction versus harm prevention, and lead discovery on ambiguous, high-impact problems across the full user journey.
Build decision frameworks for user restrictions: Design the logic that determines which restriction measure to apply (such as verification challenges or account bans) based on user risk and context.
Balance member friction against harm prevention: Quantify the trade-offs between stopping malicious actors early and keeping the onboarding experience seamless for genuine members.
Lead strategic cross-team discovery: Tackle open-ended questions across the user journey, such as analyzing duplicate/multiple account networks, assessing bot patterns, or evaluating third-party security tools versus in-house solutions.
Own domain metrics and impact measurement: Build and maintain the Security metric tree, guiding target-setting so product and business strategies are grounded in clear, measurable impact.
Raise experimentation standards: Guide best practices in A/B testing and causal inference in complex environments where traditional experimentation isn't always straightforward.
Mentor and lead through influence: Coach embedded Decision Scientists across Security teams, foster a culture of open feedback and continuous learning, and elevate data literacy across the domain.
Requirements:
Proven analytical track record: Demonstrated experience in Decision Science, Product Analytics, Econometrics, or related quantitative fields, with a history of leading complex analytical projects as a senior individual contributor.
Decision & trade-off frameworks: Solid experience designing metric systems and decision frameworks that balance competing objectives under ambiguity.
Experimentation & causal inference: Strong practical foundation in A/B testing and causal inference.
Structured…