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# Product Analyst / Data Scientist

- **Company:** Nebius B V
- **Location:** Netherlands (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Distributed Data Store, Model Inference
- **Published:** November 8, 2026
- **Apply:** https://startup.jobs/senior-product-analyst-data-scientist-nebius-10711034

## About the Role

5+ years of experience in analytics or data science.

 - Strong Python, SQL, and statistics: you understand the code and the methods you use.
 - Hands-on experience analysing large-scale customer, product, and usage datasets using statistical / analytical or distributed data-processing tools.
 - AI-assisted coding as your default way of working: you guide it, verify it, and own the result.
 - The ability to go from a question to a production-ready solution.
 - The ability to turn complex data into clear recommendations.
 - Strong commercial and product judgment, with the ability to balance customer value, monetization, growth, and profitability.
 - Experience with pricing, packaging, monetization, or commercial analytics for technical or consumption-based products.
 - Strong cross-functional collaboration skills across Product, Engineering, Data, Finance, Sales, and Operations.
 - A hands-on and proactive working style, with the ability to operate effectively in an ambiguous and technically complex environment., Familiarity with LLM inference economics: tokens, throughput and latency, batching, GPU costs., Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.

## Description

In Token Factory analytics there is no split into product analysts, ML engineers and data engineers. Each person owns a question end to end: builds the data, runs the analysis, and comes back with a recommendation that changes a decision.

Your responsibilities will include:

 - Define core product and business metrics and build a single source of truth that Product, Finance, and Sales can rely on.
 - Build a comprehensive view of self-service customers, including usage patterns, customer health, satisfaction, retention risks, unmet needs, and growth opportunities.
 - Translate customer demand and commercial plans into capacity forecasts and support capacity allocation decisions.
 - Measure effective capacity utilization and identify opportunities to improve the commercial value of available capacity.
 - Develop and evaluate pricing and packaging models, including guaranteed capacity, on-demand pricing, commitments, clawback mechanisms, and provisioned throughput units.
 - Provide data-driven recommendations for the self-service and managed offerings, including customer segmentation, pricing, and capacity.
 - Partner with Product, Engineering, Applied AI, Finance, and Sales to turn insights into concrete product and operational improvements.