VP, Product Analytics

Job description

We are looking for a hands-on **VP, Product Analytics** to lead Product Analytics and build an **AI-native analytics operating system** across the company’s crypto, stocks, prediction markets, perpetuals, and stock futures products. You will set the analytics strategy, lead and develop the Product Analytics team, and ensure data consistently shapes product and business decisions. This is a player-coach role: you will operate at executive level while remaining technically close enough to review PRs, guide data modeling and pipeline design, and challenge analytical conclusions. You will own product and business reporting, experimentation, release measurement, diagnostic analysis, the Amplitude data stack, and the systems through which analytics work is delivered at scale. ### Lead Product Analytics - Set the vision, priorities, operating model, and quality standards for Product Analytics. - Hire, coach, and develop a high-performing team. - Review analytical and data-engineering PRs, providing guidance on SQL, data models, pipelines, metric definitions, and methodology. - Represent Product Analytics in executive and product decision-making. - Allocate team capacity toward the company’s highest-impact opportunities. ### Build an AI-native analytics operating system - Design how analytics work moves from business questions to trusted decisions across intake, data discovery, analysis, validation, reporting, and knowledge management. - Build reusable AI tools to automate repetitive workflows, encode analytical standards, and improve the speed, quality, and consistency of delivery. - Establish appropriate governance, validation, and human review for high-stakes decisions. - Measure the system’s impact on turnaround time, analytical quality, experimentation throughput, and team capacity. ### Own product and business reporting - Establish trusted KPIs, source-of-truth metrics, dashboards, and executive business reviews. - Ensure reporting is accurate, consistent, and focused on decisions—not simply monitoring performance. - Partner with Product, Engineering, Data, CRM, Growth, and other functions to align definitions, priorities, and business interpretation. ### Build an experimentation culture - Make experimentation and evidence core parts of product development. - Establish standards for hypotheses, success metrics, guardrails, experiment design, causal interpretation, and rollout decisions. - Use AI and automation to streamline experiment intake, validation, analysis, and readouts while maintaining analytical rigor. - Help product teams move from opinion-led decisions to repeatable test-and-learn practices. ### Own analytics platforms and data quality - Own the Amplitude data stack, including instrumentation strategy, event taxonomy, governance, data quality, and integration with warehouse reporting. - Set standards for product instrumentation and ensure new releases can be measured reliably. - Set standards for and review analytical models and pipelines, ensuring metrics remain traceable, reproducible, and trusted as products evolve. ### Drive high-impact analysis - Lead diagnostic deep-dives into activation, conversion, retention, user behavior, market liquidity, trading execution performance, and product health. - Define measurement frameworks and success criteria for major product launches. - Oversee post-release evaluations that inform whether the company should iterate, scale, or stop. - Identify root causes, challenge weak hypotheses, and translate complex findings into clear recommendations and product actions. ### What Success Looks Like: - Leadership operates from trusted, consistent product and business metrics. - The Product Analytics team has clear priorities, strong technical standards, and consistently high-quality output. - AI-enabled workflows materially improve analytical speed, quality, and capacity. - Product teams use experimentation and evidence as standard parts of development. - Amplitude instrumentation and taxonomy are reliable, governed, and useful. - Major product launches have clear success criteria and rigorous post-release evaluation. - High-impact analyses lead to concrete product, operational, and business decisions. ### Qualifications: - Proven experience leading Product Analytics teams in a complex, fast-moving organization. - Strong hands-on technical judgment, advanced SQL, and experience with modern data platforms such as Databricks. - Demonstrated experience using AI to redesign analytics operations—not merely improve individual productivity. - Ability to design and implement AI-enabled workflows, reusable agents or tools, validation controls, and analytics knowledge systems. - Experience owning a product analytics platform; deep Amplitude experience is strongly preferred. - Strong knowledge of experimentation, causal inference, product measurement, and diagnostic analysis. - Ability to turn ambiguous business questions into rigorous analysis and clear decisions. - Strong product judgment, people leadership, and executive communication skills. ### Preferred Experience: - Consumer fintech, trading, marketplaces, or other transaction-heavy products. - Exchange mechanics, market liquidity, and experience with multi-asset products. - Leading company-wide adoption of new analytics technologies and ways of working.