Data Scientist, Product Analytics

Sonarsource·Genevamid

In short

→Analista de datos centrado en producto que transforma el uso del software en señales estratégicas.
→Diariamente explora datos, prueba hipótesis y colabora con ingenieros para modelar métricas de salud y retención.
→Destacado: Trabajas en una empresa líder en verificación de código con IA, impactando a más del 75% de las Fortune 100.

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What they ask for

✓Experiencia comprobada en análisis de producto con enfoque en métricas de uso y retención.
✓Habilidades sólidas en SQL y Python para análisis de datos.
✓Capacidad para definir y construir indicadores de salud del cliente y uso del producto.
✓Familiaridad con herramientas de análisis de datos y visualización (por ejemplo, Tableau, Looker).
✓Colaboración efectiva con product managers y equipos de ingeniería.
✓Capacidad de pensar estratégicamente y anticipar necesidades no declaradas.

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Who should you write to at Sonarsource?

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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). com, 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. 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 . 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.

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