Job Description
Overview Intuit enables millions of small and mid-sized businesses to grow through marketing automation and customer intelligence. As we accelerate toward becoming an AI-driven platform, Product Data Science must evolve to operate at a new level of technical sophistication, scale, and strategic influence. We are seeking a Principal Data Science Leader to partner with the leadership team and lead the AI transformation of the Product Data Science organization. This leader will modernize how data science is practiced by advancing modeling capabilities, experimentation rigor, generative AI adoption, and scalable decision systems while driving measurable revenue, growth, and expert network outcomes. This role requires a proven track record of building durable data science capabilities and production-grade solutions, not just delivering analyses. The ideal candidate will elevate the craft across the organization, establish higher technical standards, and position data science as a strategic force multiplier for product, growth, and expert performance. Responsibilities Strategic Leadership Define and lead the multi-year vision for Product Data Science, aligning AI, modeling, and experimentation investments with product strategy and measurable business outcomes. Partner with Data Science leadership to transform the function into an AI-native organization by modernizing tools, workflows, technical standards, and scalable decision systems. Outcome-Driven Product Impact Own revenue and growth accountability for data science-driven initiatives, ensuring modeling, experimentation, and AI investments are directly tied to measurable business outcomes and prioritized for maximum impact. Lead the rapid development and deployment of predictive models, causal inference frameworks, and LLM-enabled solutions, leveraging modern AI tools to accelerate learning velocity and reduce time from insight to decision. Drive a high-speed experimentation culture, balancing rigor with agility, so teams continuously test, learn, and iterate. Translate insights into scalable impact with clear post-launch accountability. Expert Optimization Build and own the data science framework for expert optimization: connecting expert skill signals, product and tool usage patterns, and downstream performance outcomes into a unified model of expert quality and capacity. Develop skill-to-outcome models that link expert proficiency across product domains (tool adoption, workflow adherence, knowledge depth) to measurable service quality metrics, enabling targeted coaching, routing, and capacity decisions. Partner with Expert Network and Operations leadership to translate expert behavioral signals (product usage patterns, interaction telemetry, knowledge assessments) into actionable intelligence. Identify which skills drive quality outcomes, which tool gaps create friction, and how to close both at scale. Design and scale experimentation frameworks to rigorously measure the causal impact of training interventions, tooling changes, and product investments on expert performance outcomes such as resolution quality, customer satisfaction, and proficiency scores. Build expert lifecycle analytics from onboarding through proficiency attainment, identifying acceleration levers and friction points that predict long-term performance and retention. Partner with product teams to close the loop between product and tool design and expert outcomes, surfacing where underutilization or misuse of tooling is suppressing quality and feeding those signals back into roadmap decisions. Capability Building and Craft Elevation Champion and scale durable Product Data Science capabilities by establishing rigorous standards across modeling, experimentation, model governance, and AI-enabled workflows. Raise the technical bar through structured reviews, reusable frameworks, and clear quality benchmarks. Accelerate adoption of modern AI tools, LLMs, and automation to increase productivity, technical depth, and organizational leverage across the Data Science team. Mentor, develop, and retain top data science talent by fostering a culture of ownership, technical excellence, and continuous learning. Qualifications 10+ years of experience in Product Data Science with a demonstrated track record of personally building, shipping, and scaling high-impact models and experimentation frameworks that drove measurable revenue and growth outcomes. Proven experience transforming data science practices, establishing modeling standards, experimentation rigor, reusable frameworks, and AI-native workflows across teams. Deep technical expertise in SQL and Python, with strong command of statistical modeling, causal inference, experimen