Data Analyst (Agentic Search)

1 нед. назад

НидерландыВеликобритания

pythondata pipelinesmetricsdashboards

Responsible for owning and designing search-related quality metrics and analytics architecture for AI agent search systems at .

is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.
Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible. In this role, you will define what quality means for a search system built for AI agents from day one.

Own search-related quality metrics end-to-end: from definition and computation to evolution and transition to next-generation metrics.
Design and reason about analytics architecture across the full stack: runtime behaviour → data pipelines → metrics → dashboards.
Develop and maintain analytics pipelines and tooling using Python.
Translate product, user, and technical requirements into meaningful, actionable metrics.
Challenge existing assumptions about quality and push for better, more reliable measurement.
Work closely with product managers, engineers, and stakeholders to align analytics with system behavior and business goals.
Debug metric anomalies, investigate quality regressions, and propose data-driven improvements.

Senior-level experience solving search or search-adjacent analytical problems.
Strong exposure to quality metrics in at least one of the following areas:
search relevance (web, geo, product, offers),
ranking and freshness,
ads quality or feed ranking.
Ability to think end-to-end across users, products, and technology, not just individual metrics.
Solid foundation in mathematical statistics.
Strong Python skills and a hands-on engineering mindset.
Experience operating in ambiguous environments and driving solutions independently.
Clear analytical judgment: understanding why a metric exists, how it evolves, and when it should be retired.
Strong communication skills and the ability to align product, engineering, and client perspectives.
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI