Data Scientist, Marketing Analytics

SonarSource; Greater London; Vor Ort; GBP 60.000 - 90.000; Vollzeit

Zusammenfassung

Sonar is seeking a data-driven Marketing Analytics partner in the Data & Insights team. You will turn marketing data into decisions, owning attribution, ROI, and conversion analysis end to end.

You will surface findings proactively, explore data, form hypotheses, and work with Data Engineers and Analytics Engineers to shape models and pipelines. You will define requirements, explore raw data, and contribute to data models that connect marketing data with sales and product usage, delivering

Qualifikationen

• Strong analytical track record influencing business or marketing decisions.
• Familiar with AI tools to accelerate analysis and coding tasks.
• Solid SQL skills; able to query and explore data independently.
• Proficient Python for analysis, modeling, and automation.
• Knowledge of marketing data, GTM, and joining marketing with CRM/sales data.

Aufgaben

• Own attribution, ROI, conversion analysis end to end.
• Diagnose drops/anomalies and translate insights into recommendations.
• Run A/B tests and other experiments with sound statistical methods.
• Collaborate with Data Engineers and Analytics Engineers to model marketing data.
• Tell the data story to non-technical stakeholders with clear context.

Kenntnisse

SQL

Python

Marketing analytics

Experiment design

Tools

dbt

Jobbeschreibung

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,Booking.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. 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

This role is the data team's dedicated analytical partner for Marketing. Your core job is to turn marketing data into decisions: diagnose what is working and what is not, explain why, and recommend where to spend the next dollar. You will own marketing attribution, ROI, and conversion analysis end to end, and you will be proactive about it, surfacing findings and opportunities before anyone asks. You will explore data, form hypotheses, and partner with our Data and Analytics Engineers to shape the models and pipelines you need. You will help define those models, but your primary measure of success is the quality and impact of your analysis. This role sits in the Data & Insights team and partners closely with Marketing, IT/Marketing Ops, Data Engineers, Analytics Engineers, and data scientists covering other domains.

What you will do

Drive marketing decisions with analysis. Own attribution, ROI, conversion, and funnel analysis. Diagnose drops and anomalies, quantify what drives them, and translate findings into clear recommendations for marketing leaders. Optimize marketing spend. Connect spend to outcomes across channels and campaigns, and advise on where to invest, cut, or test next. Be proactive. Explore and correlate data to surface insights nobody asked for. Anticipate the next question. Bring opportunities to the marketing team rather than waiting for requests. Run experiments. Design and read out A/B tests and other experiments, applying sound statistical methods and being honest about significance and causality. Partner on the data foundation. Work hand in hand with Data Engineers and Analytics Engineers to get marketing data into the warehouse and modeled. Define requirements, explore raw data, and contribute to data models that connect marketing data with sales and product data. Tell the story. Communicate insights so non-technical stakeholders can act, and document context, caveats, and decisions so the work survives handoffs.

Experience and qualifications

Strong analytical track record: someone who has measurably influenced business or marketing decisions through analysis, not just produced reports. Comfort with the latest AI tools, and a habit of using them to work faster and sharper: exploring data, writing and debugging code, drafting analysis, and accelerating insight. 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. Statistical foundation: experimentation, significance testing, regression, segmentation, forecasting, and the judgment to know which applies. Working knowledge of marketing and GTM data: channels, campaigns, attribution models, funnel and conversion metrics, and the realities of joining marketing data to CRM/sales and product usage 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…