We are seeking a highly motivated Data Analyst to join a Global Customer Success Data Science Team, based in London, UK, focusing on building core data capabilities across multiple global regions and markets. This role is critical in addressing analytics gaps by enabling actionable insights, automating dashboards, and ensuring consistent, reliable data foundations.
This is a team that is actively building AI-native analytics capabilities — not experimenting with AI in the margins, but embedding it into core workflows. You'll be expected to bring genuine enthusiasm for AI, apply it in your day-to-day work, and contribute to an AI-focused workstream that is automating insights, reducing manual reporting, and advancing predictive capabilities across international markets.
Key Responsibilities
• Building robust data foundations: Collaborate with engineering to develop pipelines, improve data integrity, and establish clear metric definitions and governance tailored for International markets. Build data capabilities that work consistently across multiple regions and countries, while tailoring requirements to specific local needs (e.g., regulatory, language, or workflow differences). Automate recurring reporting and validation processes to increase accuracy and efficiency. Ensure data structures and taxonomies are built for downstream AI consumption and model-readiness.
• Developing actionable dashboards & reporting: Create and maintain scalable, user-friendly dashboards that provide visibility into key performance metrics. Think broadly across the end-to-end customer journey — designing reporting that connects metrics across touchpoints rather than optimizing in silos — so that International teams have timely, market-relevant insights to make data-informed decisions.
Skills
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• experience in Data Analytics , ideally with exposure to Customer Success, Operations, or Digital Experience domains
• Proficiency in SQL for data extraction, transformation, and pipeline development
• Experience with dashboarding and visualization tools (Tableau, Qlik, or similar)
• Familiarity with big data tools (Snowflake, Databricks, Spark) and ETL processes
• Experience with Python or R for advanced analytics, automation, or experimentation support preferred
• Knowledge of statistical methods and experimentation (A/B testing) preferred
• Machine learning and Generative AI experience is a plus
