Data Analyst

Nebius 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.

We are building out the Data Analytics function within Nebius's CISO department. The team currently includes a senior BI Developer focused on Tableau dashboards, and we are now looking for a Data Analyst to complement that work with deeper analytical investigation. The department's scope spans both cybersecurity risk and broader operational and PMO metrics, and you will work directly with security, GRC, PMO, IT, and leadership stakeholders to answer data questions, uncover patterns, and support better decision-making across the department.

We are looking for a Data Analyst with 2-4 years of experience who is curious, analytical, and comfortable working with imperfect or unfamiliar data. This role is focused on understanding data deeply and turning it into meaningful insights. You will explore complex datasets, investigate the drivers behind changes in key metrics, and help stakeholders make better decisions across security operations, risk, compliance, PMO, and leadership.

• Analyze large, complex datasets to identify patterns, anomalies, and root causes, and turn them into actionable insights.
• Investigate changes in key metrics, form hypotheses, and run structured deep-dives to identify underlying drivers.
• Segment and compare data across dimensions (distributions, percentiles, baselines) to uncover meaningful differences and outliers.
• Research and assess new data sources and APIs from an analytical perspective: what is available, its quality and limitations, and the analytical value it could unlock.
• Use SQL to extract, join, and analyze data from multiple sources.
• Use Python (e.g., pandas) for exploratory analysis, data manipulation, and automating recurring analyses.
• Help define and refine KPIs and metrics together with stakeholders, contributing analytical input alongside the BI team's dashboard work.
• Apply the same analytical rigor to both cybersecurity risk metrics and operational/PMO KPIs (e.g., program delivery, resourcing, process efficiency).
• Use AI-assisted tools to speed up exploratory analysis, coding, and documentation, while validating and owning the resulting outputs.
• Identify data-quality issues and inconsistencies that may affect analysis, and work with the relevant teams to resolve them.
• Create lightweight visualizations and analytical outputs to communicate findings clearly.
• Present insights, conclusions, and recommendations to technical and non-technical stakeholders.

Security domain - strong advantage

What's it like to work at Nebius:

Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI