Senior Analyst

The role

Nebius is building search systems for AI agents — ensuring their answers are grounded in fresh, verified, real-world data. In this role, you will work at the intersection of search quality, analytics, and engineering, shaping how quality is defined, measured, and evolved across the entire system.

This is a senior, hands-on role with strong ownership. You will design and maintain analytical systems end-to-end, from runtime behaviour and data pipelines to metrics, dashboards, and decision-making frameworks. The role goes far beyond reporting - it requires systems thinking, analytical vision, and the ability to connect quality metrics to real business outcomes.

In this position, your responsibility will be to:

• Own search-related quality metrics end-to-end: from definition and computation to evolution and deprecation.

• 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.

• Evaluate and improve quality across search and adjacent domains (e.g. relevance, ranking, freshness, anti-spam).

• 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.

We expect you to have:

• 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,

  • anti-spam or content quality,

  • 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.