Data Scientist, Product Analytics
SonarSource is hiring a Data Scientist, Product Analytics in Geneva, Switzerland. Level rates it ; you can apply on SonarSource.
Level's read on this job
- Level rates this data scientist role Uses AI ●●○○: an ordinary role that requires AI tools.
- The posting mentions scikit-learn among its tools and skills.
- SonarSource has 120 other open roles on Level.
AI in this role
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
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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.
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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.
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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.
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Be proactive. Explore product, sales, and customer data to surface findings nobody asked for. Anticipate the next question.
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Run experiments and validate. Test which usage signals actually predict outcomes, applying sound statistical methods and being honest about significance and causality.
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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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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
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Deep product understanding and a track record of working effectively with PMs, translating product knowledge and usage data into decisions they act on.
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Strong analytical track record: someone who has measurably influenced revenue, product, or GTM decisions through analysis, not just produced reports.
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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.
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Eagerness to develop: you actively grow your skills, seek feedback, and treat new tools and methods as opportunities rather than threats.
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Solid SQL. You can independently query, join, and explore data without waiting for someone to prepare it for you.
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Proficiency in Python for analysis, modeling, and automation. Experience with ML and statistical libraries (e.g. scikit-learn, statsmodels) for modeling, prediction, and inference.
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Statistical foundation: experimentation, significance testing, regression, segmentation, forecasting, and the judgment to know which applies.
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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.
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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.
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Strong communication and stakeholder skills: you can challenge weak measurement respectfully and make a recommendation, not just present options.
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Proactivity and autonomy: you raise your hand early, plan your own work, and look for impact without being asked.
In-office culture
We're intentional about this. We believe the best teams are built in the room together. Three anchor days — Mondays, Tuesdays, and Thursdays — create the collaboration rhythm that makes a hub office worth having.
Candidates need to be genuinely based in the location the role is posted — if that's not where you are today, we're happy to support relocation for the right person.
We value diversity, equity, and inclusion
At Sonar, we believe that our diversity is our strength. We are a global company that values and respects different backgrounds, perspectives, and cultures. We are committed to fostering a diverse and inclusive work environment where everyone feels valued and empowered to contribute their best. We are proud to be an equal opportunity employer and welcome all qualified applicants, regardless of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
If you need any accommodation, please reach out to us at hiring@sonarsource.com.
All offers of employment at Sonar are contingent upon the results of a comprehensive background check and reference verification conducted before the start date.
Applications that are submitted through agencies or third party recruiters will not be considered.
How we rate this
Data Scientist, Product Analytics at SonarSource rates 47 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.
Uses AI. An ordinary role that requires AI tools.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $167k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
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- This role expects you to use AI tools as part of the job. Which ones have you used, and for what?
- Tell me about a time an AI tool got something wrong. How did you catch it?
Adapt your resume
- List these exact terms on your resume: scikit-learn. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Put the AI tool in a bullet point about what you did, not just in a skills list. This role treats it as a required part of the job.
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