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Senior Product Analyst / Data Scientist

AI Summary
Nebius is hiring a senior product analyst/data scientist to own analytics questions end to end for its AI cloud Token Factory. The role builds trusted product and commercial metrics, analyzes customer usage and health, forecasts capacity, and informs allocation decisions. It also evaluates pricing, packaging, monetization, and capacity-utilization opportunities for self-service and managed offerings. Success requires strong Python, SQL, statistics, commercial judgment, and close collaboration with Product, Engineering, Finance, Sales, and Applied AI. Familiarity with LLM inference economics, GPU costs, throughput, and latency is a valuable advantage.
Remote from
Europe
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
6 Dec 2026
Listing views
44
Application actions
3
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Opportunity details

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.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior, highly ambiguous role combining product analytics, data engineering, statistical analysis, capacity planning, and monetization strategy. The analyst is expected to independently deliver production-ready solutions and influence high-impact commercial decisions in a technically complex AI infrastructure environment.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$155,000
US market range (per year)$125k–$185k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer base-pay amount is disclosed. The displayed figures are estimated annual USD market compensation for a senior product analytics/data science role with pricing, capacity, and AI infrastructure responsibilities; the yearly cadence is inferred from the full-time senior professional role.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you create a single source of truth for product, finance, and sales metrics?

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.

How would you forecast capacity demand for a consumption-based AI cloud product?

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.

Describe how you would evaluate a new pricing and packaging model.

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.

How do you ensure AI-assisted code is reliable in an analytical production workflow?

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.

How would you explain a complex utilization or retention analysis to non-technical leaders?

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.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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