Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
Senior Product Analyst / Data Scientist
- Remote from
- Europe
- Salary
- Undisclosed
- Department
- Data Science & Analytics
- Employment
- Full Time
- Experience
- Senior
- Published
- Apply before
- 6 Dec 2026
- Listing views
- 44
- Application actions
- 3
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About this role.
About Nebius:
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.
The role:
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.
We expect you to have:
– 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.
Nice to have:
– Familiarity with LLM inference economics: tokens, throughput and latency, batching, GPU costs.
Benefits & Perks:
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
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
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
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.
If you need accommodations during the application process, please let us know.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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Sample interview questions
I would first align stakeholders on metric definitions, business decisions, owners, grain, and acceptable refresh latency. I would then build governed source models, document transformations and definitions, add data-quality tests, and publish certified datasets and dashboards with clear lineage.
I would combine historical usage, customer cohorts, seasonality, committed capacity, sales pipeline signals, and product adoption trends. I would produce scenario-based forecasts, quantify uncertainty, back-test accuracy, and translate results into allocation recommendations with clear service-level and profitability trade-offs.
I would define the target customer segments and the intended behavioral and financial outcomes, then model willingness to pay, usage distributions, infrastructure costs, and expected margin. Where possible, I would use controlled experiments or phased rollouts and measure conversion, expansion, retention, utilization, and gross margin against a baseline.
I treat AI-generated output as a draft and remain accountable for the final result. I validate assumptions, review logic and edge cases, write tests, reconcile outputs to known benchmarks, use version control and peer review, and monitor production pipelines for data-quality regressions.
I would begin with the decision required and state the key finding in plain language, supported by a small number of business-relevant metrics. I would use simple visuals, explain uncertainty and trade-offs, and conclude with prioritized actions, expected impact, and the assumptions behind the recommendation.
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