Data Scientist, Product Analytics

### **Who is Sonar?** Sonar is driving the future of agent-centric software development. As the leader in AI code verification and governance, we solve a critical problem: ensuring that software generated by AI-assisted developers or autonomous agents is reliable, secure, and maintainable. Integrating seamlessly with Claude Code, Codex, Cursor, GitHub Copilot, Gemini, and Devin, we help over 75% of the Fortune 100 build trusted, reliable, compliant software. Customers who use Sonar are 44% less likely to report an outage due to AI-generated code. We believe code verification is the critical missing link in the Agent-Centric Development Cycle (AC/DC). Industry giants like Nvidia, ServiceNow,, Goldman Sachs, AstraZeneca, and Ford Motor Company count on us to provide independent, explainable, consistent review and governance of their AI-generated code via products like: - SonarQube: The world’s leading AI code review and verification platform. - SonarQube Foundation Agent: Currently topping the leaderboards for agentic software repair. - SonarSweep & Sonar Context Augmentation: Providing the enterprise-grade context and constraints agents need to be truly effective. Our team operates across global hubs in Austin, Bochum, Dubai, Geneva, London, Singapore, Tokyo, and Washington D.C. We move with a mindset we call CODE: - Committed to our customers and community. - Obsessed with quality. - Deliberate in our decisions. - Effective as one team. With over $400M in revenue and profitable, fast-paced growth, we are building the backbone of the AI software revolution. If you’re hungry to have an impact, want to build at a fast pace, and ready to work at the forefront of AI, we want to hear from you. ### 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 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 tools and methods as opportunities rather than threats. - Solid SQL. You can independently query, join, and explore data without waiting for someone to prepare it for you. - Proficiency in Python for analysis, modeling, and automation. Experience with ML and statistical libraries (e.g. scikit-learn, statsmodels) for modeling, prediction, and inference. - Statistical foundation: experimentation, significance testing, regression, segmentation, forecasting, and the judgment to know which applies. - Working knowledge of how product usage connects to revenue: funnels, cohorts, retention, account health, and the realities of joining product usage data with CRM and sales data. - Willingness to get hands-on with data modeling. You don't need to be a dbt expert, but you must be comfortable exploring messy data and partnering on (or building) the models you need rather than waiting for clean tables. - Strong communication and stakeholder skills: you can challenge weak measurement respectfully and make a recommendation, not just present options. - Proactivity and autonomy: you raise your hand early, plan your own work, and look for impact without being asked.