UX Analytics Lead

The Capgemini Group is a global leader in partnering with companies to transform and manage their businesses. It has nearly 336,000+ people, present in over 50 countries, with a 57 year heritage. The Group delivers end to end services from strategy and design to engineering, powered by capabilities in cloud, data, AI, and digital engineering. Group IT empowers the business through technology that enables efficiency, profitability, and sustainable growth by delivering enterprise wide solutions and integrating all business units into a unified IT services model. Group IT is driving transformative rollouts and initiatives to enhance security, streamline operations, and elevate digital experiences for Capgemini employees globally.

• 5+ years of experience in product analytics, quantitative UX research, or a related field
• Proven experience partnering closely with UX/design and product teams in product led environments
• Strong track record of translating data into actionable product or design decisions
• Hands on experience with analytics tools such as Amplitude, Mixpanel, Google Analytics, or similar
• Strong analytical mindset with the ability to structure ambiguous problems and define meaningful metrics
• Experience with experimentation approaches such as A/B testing
• Working knowledge of SQL, Python, or similar tools for data querying and analysis
• Experience working within complex or enterprise environments

• Analyze user behavior to identify friction points, usage patterns, and opportunities for UX improvement
• Translate complex data into clear, actionable insights for product managers, designers, and stakeholders
• Combine quantitative and qualitative insights to explain not only what is happening, but also why
• Define, standardize, and evolve UX metrics such as task success, drop off rates, engagement, and efficiency
• Structure and analyze user journeys and funnels across key enterprise applications
• Establish success criteria and measurement frameworks for features and product releases
• Design and support experimentation, including A/B testing and feature validation
• Build scalable UX analytics practices, including frameworks, dashboards, and best practices
• Mentor designers and researchers in using data effectively within their workflows