Head of Analytics

The role

Nebius is looking for a Head of Analytics.

As Head of Analytics, you’ll lead the analytics team and strengthen how data is used to support decision-making across the company. Reporting to the VP of Data & Analytics, you’ll translate the broader data strategy into clear priorities, high-quality analytical delivery, and measurable business impact.

You’ll manage and develop teams of analysts and BI, partner closely with senior stakeholders across Product, Sales, Marketing, Finance, Customer Success, and Operations, and ensure that the company has trusted metrics, actionable insights, and scalable analytical capabilities.

This is a strategic and hands-on leadership role. You’ll be expected to set direction, create structure, raise analytical standards, and remain closely involved in the company’s most important business questions.

You’re welcome to work in our offices in Tel Aviv, Israel

Your responsibilities will include: 

• Team leadership and development. Lead, mentor, and develop the analytics team. Set clear expectations, establish ownership, support professional growth, and create a high-performance culture focused on business impact, analytical quality, and accountability.
• Analytics strategy execution. Partner with the VP of Data & Analytics to shape the analytics strategy and own its execution, prioritization, and delivery. Translate company goals into a clear analytics roadmap and ensure resources are focused on the highest-impact business needs.
• Business partnership. Act as a trusted analytical partner to senior leaders across Product, Sales, Marketing, Finance, Customer Success, and Operations. Understand their objectives, challenge assumptions, and help turn strategic questions into measurable decisions and actions.
• Decision support and advanced analysis. Lead high-impact analyses across customer behavior, product performance, revenue, retention, pipeline, operational efficiency, and business growth. Ensure findings are translated into clear recommendations rather than presented as data alone.
• Metrics and analytical governance. Establish and maintain consistent KPI definitions, business logic, and analytical standards. Ensure stakeholders use trusted and aligned metrics across dashboards, reports, forecasts, and business reviews.
• Dashboard and reporting oversight. Guide the design, implementation, and improvement of executive and operational dashboards. Ensure reporting is relevant, reliable, easy to use, and aligned with business priorities.
• Self-service analytics. Enable business teams to access and use data independently while maintaining appropriate governance, security, quality, and consistency. Identify where self-service creates value and where centralized analytical support is required.
• AI-driven analytics. Help define and implement AI-assisted analytics capabilities that reduce manual reporting, accelerate insight generation, support natural-language access to data, and proactively surface risks or anomalies. Establish clear quality and validation standards for AI-generated outputs.
• Cross-functional data collaboration. Work closely with Data Engineering, Data Architecture, and other technical teams to ensure that analytical datasets, semantic models, and data pipelines support current and future business requirements.
• Operating model and prioritization. Build scalable processes for intake, prioritization, delivery, documentation, and stakeholder communication. Balance urgent business requests with strategic initiatives and long-term capability development.

We expect you to have: 

• Significant experience in analytics, business intelligence, or a related data function within a technology or data-driven company.
• Proven experience leading and developing analytics teams, including setting priorities, managing performance, and building strong analytical talent.
• Strong business judgment and the ability to connect analytical work to revenue, customer, product, operational, and strategic outcomes.
• Experience partnering with senior executives and functional leaders, with the confidence to challenge assumptions and influence decisions.
• Strong proficiency in SQL and a solid understanding of data modeling, BI platforms, analytical methodologies, and modern data environments.
• Experience defining company-level KPIs, metric frameworks, and scalable reporting standards.
• Strong analytical foundation, including statistical reasoning, cohort analysis, funnel analysis, forecasting, experimentation, and performance measurement.
• Ability to communicate complex analytical findings clearly to both technical and non-technical audiences.
• Strong understanding of data quality, governance, documentation, and the importance of consistent business definitions.
• Demonstrated ability to operate in a fast-growing environment, manage competing priorities, and create structure where processes are still developing.

It will be an added bonus if you have: 

• Experience with BI tools such as Tableau, Power BI, Looker, or Superset.
• Familiarity with modern data stacks and technologies such as dbt, Airflow, BigQuery, Snowflake, ClickHouse, or Postgres.
• Experience working across multiple business domains, including Product, Go-to-Market, Finance, Customer Success, and Operations.
• Experience building or implementing semantic layers and governed self-service analytics environments.
• Practical experience with AI-assisted analytics, analytical agents, natural-language querying, or automated anomaly detection.
• Experience in a cloud infrastructure, SaaS, technology platform, or high-growth environment.
• Bachelor’s degree in a quantitative, technical, economic, or business-related field. An advanced degree is an advantage.

Competencies & Behavioral Traits

• Ownership and accountability: Takes responsibility for the team’s commitments, quality, and business impact. Follows through, raises risks early, and does not rely on ambiguity as an excuse for missed outcomes.
• Business orientation: Focuses the team on solving meaningful business problems rather than producing dashboards or analysis without a clear decision or action.
• Strategic and hands-on leadership: Can set long-term direction while remaining close enough to the work to review methodologies, challenge conclusions, and support critical analyses.
• Structured problem solving: Turns broad or unclear business questions into measurable hypotheses, analytical plans, and actionable recommendations.
• People leadership: Creates clarity, gives direct and constructive feedback, develops talent, and builds an environment where analysts can grow and take ownership.
• Clear stakeholder communication: Explains insights simply, aligns stakeholders on definitions and expectations, and communicates conclusions in a concise and decision-oriented way.
• Influence and challenge: Builds trusted relationships while being comfortable questioning assumptions, highlighting uncomfortable findings, and recommending changes.
• Prioritization and judgment: Distinguishes between urgent and important work, makes clear trade-offs, and protects the team from low-value or repetitive requests.
• Quality mindset: Sets high standards for analytical accuracy, documentation, reproducibility, and data validation.
• Collaboration: Works effectively across business and technical teams, recognizes shared ownership, and avoids creating silos between analytics, engineering, and business functions.
• Adaptability: Operates effectively in a fast-changing environment and adjusts priorities without losing strategic focus or delivery discipline.