Job Description

You will join the central data team within the Frontend Technologies domain. Frontend Technologies domain is responsible for designing and helping implement architectural changes for each frontend platform: Android, iOS, and Web, resulting in clear boundaries, enablement of marketplace-wide strategic initiatives, and a great member experience.

We’re looking for a Decision Scientist to join the team to ensure the business impact of frontend architectural changes is estimated, trade-offs are rigorously analyzed, and our long-term platform strategy is grounded in evidence-based insights. Despite working from a central data team within the domain, you are expected to partner with engineering managers, staff engineers and designers to co-create, challenge default assumptions, and bring a proactive decision-making lens to the table.

This role is for someone who can independently own analyses from start to finish, contribute to strategic engineering decisions, and work closely with Engineering, Design, Analytics Engineering, and other Decision Scientists. You are a partner who can turn complex technical or business questions (e.g., modeling the financial and behavioral cost of an app crash, or mapping performance latency to conversion rates) into structured analysis, interpreting results, and communicating clear recommendations to stakeholders.

Enable Data-Driven Platform Decisions: Drive the definition, implementation, and tracking of key technical and behavioral metrics that define frontend success.
Bridge Technical & Business Metrics: Turn highly technical questions (e.g., "What is the business cost of an app crash?" or "How does rendering speed impact member conversion?") into structured insights.
Proactive Co-creation in Discovery: Partner with engineering and design leaders during the discovery phase to evaluate opportunity sizes, establish baseline metrics, and ensure no major architectural shifts are launched without clear decision criteria.
Support Experimentation: Help design, monitor, and analyze A/B tests to evaluate the cumulative impact of platform optimizations and refactoring.
Collaborate within DSA: Engage with the broader Vinted Data Science & Analytics community to share methodologies, standardise tooling, and scale our decision science standards.

Requirements

Working experience in decision science, product analytics, or business intelligence (ideally within a tech-product or platform-engineering environment).
Highly proficient in SQL and Python (or R) for writing clean, efficient queries to handle large, complex datasets.
Experience with version control (Git) and dbt is a plus.
Deep understanding of statistical methods, A/B testing methodologies, and analytical framing.
Able to translate complex technical architectures and statistical findings into clear, persuasive, and visual recommendations for both technical and non-technical audiences.
Possess a strong intuition for return-on-investment on analytical tasks, knowing when to choose short-term pragmatism over theoretical perfection.
You don't need to write frontend application code, but you have the curiosity and capability to understand platform-level architectures (caching, latency, app-store release cycles).

Company offers

The opportunity to benefit from our share options programme
25 working days of holiday
Access to all the tools & tech needed for work
Home office support: we provide IT workstation equipment and a personal budget of up to 540 for home workplace furniture
Private health insurance
Confidential Employee Assistance Program (EAP) for you and your family
Frequent team-building events
A personal monthly budget for shopping on Vinted
The opportunity to spend up to 90 days per year - 21 of which can be spent working outside of the EU - on workation
A dog-friendly office
In Vilnius office: Gym & in-house meals at friendly prices
In Kaunas office: a monthly lunch allowance, and a once-a-week provided in-house lunch and breakfast