Mobile Analytics Lead, Leadtech

• 6+ years of experience in mobile analytics, product analytics, growth analytics, or data science
• 2+ years of experience leading or mentoring analytics teams
• Deep expertise in mobile product analytics and user lifecycle measurement
• Strong experience designing and analyzing A/B tests and experimentation frameworks
• Advanced proficiency in SQL and experience with large-scale data platforms such as BigQuery, Databricks, or Snowflake
• Proficiency in Python for statistical analysis, predictive modeling, and automation
• Experience working with mobile analytics platforms such as Amplitude, Firebase, or Mixpanel
• Experience with mobile attribution platforms such as AppsFlyer, Adjust, or Branch

Desirable

• We are looking for a Lead Mobile Analytics professional to drive the strategy, architecture, and execution of analytics across our mobile ecosystem
• In this role, you will partner closely with Product, Growth, Marketing, Engineering, and Data teams to unlock insights that shape product strategy and accelerate user and revenue growth
• You will lead the development of a world-class mobile analytics framework, enabling deep understanding of user behavior, experimentation at scale, and predictive insights such as pLTV (predictive Lifetime Value)
• Your work will directly influence product roadmap decisions, acquisition strategy, monetization models, and long-term user engagement
• This role requires a combination of product thinking, statistical rigor, technical depth, and leadership to build scalable analytics solutions and mentor a high-performing analytics team
• Own the mobile analytics vision and roadmap, ensuring alignment with product growth and business objectives
• Design scalable measurement frameworks for mobile acquisition, activation, retention, engagement, and monetization
• Establish best practices for mobile data instrumentation, experimentation, and analytics governance
• Lead analysis of mobile user behavior, funnels, and lifecycle metrics to identify opportunities for product improvements
• Translate complex datasets into clear insights that drive product decisions and measurable growth
• Partner with product leaders to shape data-informed product strategies
• Build and scale A/B testing and experimentation frameworks across mobile products
• Apply advanced statistical methods and causal inference techniques to evaluate product and growth initiatives
• Champion a culture of experimentation and evidence-based decision making across teams
• Implementation of predictive Lifetime Value (pLTV) models to optimize acquisition and monetization strategies
• Use advanced analytics and modeling to forecast user retention, revenue, and long-term engagement
• Partner with growth teams to allocate marketing investments using ROI and predicted user value
• Ensure reliable, scalable mobile event tracking and analytics infrastructure across platforms
• Work with large-scale datasets using SQL, BigQuery, Databricks, and modern data platforms
• Leverage Python and advanced analytics techniques for modeling, automation, and experimentation analysis
• Collaborate with engineering teams to ensure high-quality data pipelines and instrumentation frameworks
• Own measurement and attribution across mobile marketing channels using AppsFlyer or similar attribution platforms
• Evaluate and optimize performance across paid acquisition, organic growth, and re-engagement campaigns
• Connect marketing performance with long-term user value and product engagement metrics
• Hire, mentor, and develop a high-performing team of product and mobile analysts
• Elevate analytics capabilities across the organization through best practices, tooling, and knowledge sharing. Influence executive decision-making through clear storytelling with data
• Strategic impact: Your insights will shape the product roadmap and growth strategy
• Scale: Work with large datasets and millions of mobile users
• Ownership: Lead the analytics function for a critical part of the product ecosystem
• Innovation: Build predictive models and experimentation frameworks that power data-driven decision making