Senior Product & PLG Analyst (Full-Stack Data Scientist)

<div class="content-intro"><p><strong>About Nebius:</strong></p> <p>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.</p> <p>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.</p> <p>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.</p></div><p><strong>About the Tavily at Nebius</strong><br><br>In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible.<br>Tavily provides the search and intelligence layer that powers AI agents and retrieval systems with trustworthy, real-time information. We work with some of the most innovative teams in AI, from fast-growing startups to global enterprises, helping them build agents that are not only intelligent but informed.<br><br><strong>About the Role</strong><br><br>We're looking for a senior, full-stack analyst / data scientist to own Product and PLG analytics end-to-end. You'll own the metrics behind our self-serve motion (signup, activation, usage, conversion, expansion and retention) <strong>and find the drivers behind them</strong>. You'll work directly with our CEO and leadership as a thought partner on product and growth decisions.<br><br>This role is fully independent. When a question comes up, you take it all the way: from building the pipeline, model the data, train and deploy the model, analyze the results and come back with a clear recommendation. Nothing waits in a queue for someone else. You'll also work closely with the other analysts on the team, share methods and standards, and collaborate across Product, Engineering, Growth, Marketing and GTM.<br><br><strong>What You'll Do</strong><br><br>• Own Product and PLG analytics end-to-end: define, instrument and measure the self-serve funnel, from first API call through activation, conversion to paid, expansion and churn.<br>• Uncover the drivers behind top-level KPIs: which behaviors, use cases and product experiences predict activation, retention and monetization.<br>• Build what you need: data pipelines and models (SQL, dbt, Python, orchestration) that turn raw product, usage and billing data into trusted, reusable datasets.<br>• Build and ship predictive models (conversion propensity, churn risk, usage forecasting, lead and account scoring) and bring their outputs into product and GTM workflows.<br>• Design, run and evaluate experiments (A/B tests, pricing and packaging tests, onboarding changes) and apply causal inference where you can't run a clean experiment.<br>• Proactively monitor key business and product metrics, investigate trends and anomalies, and surface insights stakeholders can act on.<br>• Partner directly with the CEO and leadership on strategic questions, including pricing, growth levers, product bets and market opportunities, and turn ambiguous questions into clear analysis and decisions.<br>• Set the standard for how we measure product success: metric definitions, a shared source of truth and analytical rigor across teams.<br><br><strong>What We're Looking For (Must-Haves)</strong><br><br>• 5+ years of experience as a Product / Growth Analyst, Data Scientist or equivalent.<br>• 2+ years in a SaaS company, with deep understanding of funnels, cohorts, activation, retention, monetization and PLG dynamics.<br>• A strong statistical foundation.<br>• A proven record of working end-to-end on your own, from data engineering through modeling to a delivered recommendation.<br>• Strong hands-on experience using Python for data analysis, experimentation, machine learning and data modeling in a production environment.<br>• Comfort working with large, messy, event-level datasets.<br>• Hands-on experience building, validating and deploying predictive models (e.g., classification, forecasting, segmentation) that were used to make real business decisions.<br>• Demonstrated ability to influence business strategy through data and drive alignment across cross-functional stakeholders, including executive leadership.<br>• Strong communication: you can turn complex analysis into a clear, compelling story and a concrete recommendation.<br>• Experience with modern data tooling (Snowflake/BigQuery, dbt, Airflow, and BI tools like Omni/Looker/Tableau).<br>• BSc in Statistics / Math / CS or equivalent.<br><br><strong>Nice to Have</strong><br><br>• Experience at an API-first, developer-focused or PLG company.<br>• Experience with product analytics platforms (PostHog, Amplitude, Mixpanel).<br>• Experience with pricing and packaging analysis or usage-based billing models.<br>• Familiarity with LLMs / AI tooling, and using AI agents to speed up your own analytical work.<br>• Evidence that you build things on your own: a GitHub profile, portfolio, side projects or publications.<br>• MSc / PhD in Statistics / Math / CS or equivalent.</p><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><strong><span style="font-size: 18px;">Pay Transpa