AI Analytics Lead

EWA is an international B2C EdTech product for language learning, with a multi-million user base around the world. We build a mobile-first learning experience and work with large volumes of user data: acquisition, activation, engagement, retention, monetization, and learning behavior. About the Role We're looking for an AI Analytics Lead — a strong product analyst / analytics lead who'll take ownership of analytics at EWA and move it to the next level: from classic product analytics to an automated, AI-driven system for working with data. This is a hands-on leadership role with broad scope. You'll strengthen the technical foundation of analytics, bring order to metrics and data, grow the self-service approach, and lead the shift to AI agents for data access, insight discovery, and automating repetitive analytics work. You won't be starting from scratch: EWA already has product analytics, user data, and teams working with metrics. Your job is to strengthen the system, remove friction, and make analytics scale together with the product. What You'll Lead Analytics Strategy & Operating Model Define how analytics should support the product, growth, monetization, and experimentation. Draw the line between what's handled through self-service, what's handled through automation and AI agents, and where deep expert analysis is needed. AI-Powered Analytics Lead the shift to AI agents in analytics: natural-language data access, AI-assisted reporting, insight discovery, automation of repetitive queries, anomaly detection, and internal tools for product and business teams. B2C Product Analytics Work with the key product and business metrics: funnels, activation, onboarding, retention, engagement, monetization, churn, LTV, paywall, cohorts, subscription metrics, segmentation, and learning behavior. Metric Governance & Data Foundation Build decentralized KPI ownership, unified metric definitions, documentation, data-quality control, and a trusted semantic layer for Product, Growth, Marketing, Finance, Engineering, and Leadership. Event Tracking & Data Modeling Review and improve event taxonomy, tracking logic, the tracking plan, data models, and analytics layers, so that both teams and AI agents can work with the data reliably. Self-Service Analytics Build a system where product, growth, and leadership teams can get answers to common questions without constant dependence on manual, ad hoc analysis. Experimentation & A/B Testing Strengthen the experimentation practice: hypothesis design, metric selection, test analysis, statistical interpretation, segment analysis, and product recommendations based on the results. Leadership Decision Support Be a partner to leadership: find growth opportunities, validate hypotheses with data, surface risks, and turn complex analysis into clear product and business decisions. What Success Looks Like - Product and growth teams get answers to product and business questions faster. - A meaningful share of repetitive analytics work is moved to AI agents and automation. - Fewer conflicting interpretations of the same metrics across EWA. - A clearer system for metric ownership, documentation, and data quality. - Product managers can work with self-service and AI-assisted analytics without waiting for manual processing of every request. - Experiments become faster, cleaner, and easier to interpret. - The leadership team trusts data when making product decisions. - Analytics becomes a scalable system rather than a bottleneck. Technical Focus We expect strong hands-on experience or deep understanding of a modern analytics stack and AI-enabled analytics workflows: - SQL and complex analytical queries - product analytics tools - event tracking and event taxonomy - data warehouse - BI and dashboards - data modeling - semantic layer - data quality - A/B testing infrastructure - automated reporting - self-service analytics - hands-on work with AI tools for analytics - LLM-based workflows for analytics tasks - AI agents for…