Principal Data Scientist, Product Data & Analytics

QuickBooks is Intuit's flagship platform for small and mid-sized businesses, and our International business — spanning Canada, the UK, Australia, and beyond — is a critical growth engine as we scale a truly global platform. Product Data Science sits at the center of that growth, translating market-specific customer behavior into the models, experiments, and decisions that shape the QuickBooks product roadmap in every market we serve. We are seeking a Principal Data Scientist to lead International Product Data & Analytics for QuickBooks. This role will set the data science agenda across markets — partnering deeply with Product Development and Product Management teams in each region — to modernize how the organization builds causal evidence, runs experiments, and operates AI-native decision systems at scale. This IC role requires a proven track record of building durable data science capabilities and production-grade solutions, not just delivering analyses. The ideal candidate frames the questions that change strategy across the organization, sets the methodological patterns other teams adopt as their default, and elevates the craft of data science across a multi-market organization. Intuit has embraced a hybrid working model and this role will be based out of our Toronto office 3 days per week. Responsibilities Strategic Leadership & Vision Define and lead the multi-year vision for QuickBooks International Product Data Science — setting the framework for aligning inference, experimentation, and AI-native investments with product strategy and measurable business outcomes across every market. Partner with International Product leadership and market-level Product Development and Product Management teams to transform the function into an AI-native organization, designing the data-product and data agent-context the organization builds on. Outcome-Driven Product Impact Own accountability for revenue and growth outcomes across International markets, ensuring modeling, experimentation, and AI investments are prioritized for maximum cross-market impact. Set the inference and experimentation patterns — designs, guardrails, lifecycle standards — that teams across markets adopt as their default Frame the questions that change strategy across the organization: define the metric trees and causal levers that multiple business areas and markets manage to, and drive insights that alter org-level roadmaps and investment decisions. Design the agent-context, evaluation, and delegation-governance architecture the organization adopts — how rigor is encoded, certified, and monitored when analysis runs without a human in the loop. Capability Building & Craft Elevation Champion and scale durable Product Data Science capabilities across markets by establishing rigorous standards for inference & algorithms, AI-native evaluation, and data-product / decision governance — your evaluation architectures become the org's pattern. Set the semantic layers, context, and monitoring patterns that other teams across International adopt as their default. Mentor top data science talent across regions, fostering ownership, technical excellence, and continuous learning; the framing patterns you set become how the org decomposes problems. Qualifications 10+ years of experience in Product Data Science, with a demonstrated track record of personally building, shipping, and scaling high-impact models, analytical  and experimentation frameworks that drove measurable revenue and growth outcomes across multiple markets. Master's degree or higher in Statistics, Computer Science, Economics, Applied Mathematics, Machine Learning, or a related quantitative field. Proven experience transforming data science practices — establishing inference and experimentation patterns, evaluation architectures, and AI-native workflows — that other teams adopt as their default. Deep technical expertise in SQL and Python, with strong command of causal identification, experiment design, predictive m