# Data Scientist, Product Analytics at SonarSource

SonarSource is hiring a Data Scientist, Product Analytics in Geneva, Switzerland. Level rates it Uses AI ●●○○; you can [apply on SonarSource](https://jobsbylevel.com/go/3ed87228-909b-451e-95f5-db26188719e5?utm_medium=ai-agent).

AI Level 2, AI centrality 47 out of 100. Geneva.

## Details

- Company: [SonarSource](https://jobsbylevel.com/companies/sonarsource)
- AI level: AI Level 2 (score 47 out of 100)
- Location: Geneva
- Posted: September 7, 2026
- Apply: https://jobsbylevel.com/go/3ed87228-909b-451e-95f5-db26188719e5?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-scientist-product-analytics-at-sonarsource-77adf5

## Prepare for this job

The job page lists 3 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/data-scientist-product-analytics-at-sonarsource-77adf5

## Description

Who is Sonar? Sonar enables organizations to ship agentic code with confidence. Code creation is abundant, but confidence is scarce. Our core platform, SonarQube, verifies over 750 billion lines of code every day, delivering agentic verification and governance for all code. Trusted by more than 7 million developers and 75% of the Fortune 100, including teams at Nvidia, ServiceNow, Booking.com , Goldman Sachs, AstraZeneca, and Ford Motor Company, Sonar enables organizations to reduce outages, improve security, and lower the cost and risk of agentic software development. Founded in Geneva and now headquartered in Austin, Texas, Sonar has a global team working across offices and hubs, including Silicon Valley, London, Bochum, Barcelona, Singapore, Tokyo, and the Washington, D.C. area. At Sonar, we’re enabling enterprises to build agentic software factories that can move quickly while maintaining the control, consistency and evidence required to ship with confidence. Position description Operating within the Data & Insights team, your mission is to decode customer behaviors and transform product engagement into high-impact, actionable intelligence. By bridging the gap between product adoption and commercial data, you will construct a holistic view of user health and the tangible value we provide. You will take full ownership of the product-usage analytics lifecycle. This involves hands-on data exploration, hypothesis testing, and deep collaboration with Data and Analytics Engineers to architect the foundational models you require. Success is defined by analytical rigor and your ability to steer strategic product and Go-to-Market (GTM) decisions. Embedded in our central Data & Insights department, this role serves as a strategic partner to our Product organisation, driving cross-functional synergy with other departments, engineering, and data science teams to maximize overall success. Success depends on genuinely understanding the product you're analyzing, not just the data behind it. What you will do Understand product usage . Work closely with product managers (PMs), building a deep understanding of the product itself, to analyze how customers adopt and use it, and identify patterns in engagement, feature usage, and retention.to Analyze how customers adopt and use the product, and identify patterns in engagement, feature usage, and retention. Connect usage to outcomes. Link product usage data to customer and sales data to reveal how usage relates to expansion, churn risk, and account health. Define usage-based signals. Build the logic behind usage scoring, health indicators, and other metrics that translate behavior into insights that drive activation, retention, and expansion. Be proactive. Explore product, sales, and customer data to surface findings nobody asked for. Anticipate the next question. Run experiments and validate. Test which usage signals actually predict outcomes, applying sound statistical methods and being honest about significance and causality. Partner on the data foundation. Work with Data and Analytics Engineers to get product and sales data into the warehouse and modeled well. Define requirements and contribute to data models that connect product usage with customer and sales data. Tell the story. Communicate insights clearly to stakeholders across the business, and document context, caveats, and decisions so the work survives handoffs. Experience and qualifications Deep product understanding and a track record of working effectively with PMs, translating product knowledge and usage data into decisions they act on. Strong analytical track record: someone who has measurably influenced revenue, product, or GTM decisions through analysis, not just produced reports. Comfort with the latest AI tools, and a habit of using them to work faster and sharper: You stay current as the tooling evolves and bring new approaches to the team. Eagerness to develop: you actively grow your skills, seek feedback, and treat new

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