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 ofsearch 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.