Senior Data Scientist, Causal Inference

TurboTax is investing heavily in how we use experimentation and causal inference to guide decisions across marketing, product, and business strategy. We are expanding our Decision Science Team and hiring a Staff Data Scientist to define the next generation of scientific rigor, experimentation systems, and measurement capabilities at scale. In this role, you will sit at the center of our most important decisions - shaping how we design experiments, develop causal methods, and build the systems that make best practices repeatable across the organization. You will pair deep econometric expertise with a builder mindset, transforming cutting‑edge science into durable, widely adopted capabilities. You will also lead the development of causal measurement frameworks for emerging agentic AI technologies -- ensuring we understand how AI‑driven systems behave, how they influence customer outcomes, and how we attribute impact in increasingly automated experiences. This is a rare opportunity to define the scientific foundation, tooling, and strategic direction for a major consumer business that reaches millions of customers. Responsibilities 1. Lead experimentation innovation and systems •Build and scale experimentation best practices across TurboTax -- including post-stratification, diagnostics, and consistent test read quality. •Design the workflows, tooling, and systems that make high-quality experimentation repeatable and easy for partner teams. •Partner with engineering to operationalize these capabilities in platforms and pipelines. 2. Set the scientific bar for causal inference •Define the gold standard for causal inference methods across TurboTax and Intuit. •Guide and mentor economists and data scientists on study design, rigor, and interpretation. •Drive adoption of modern econometric and causal ML methods where they generate real business impact. 3. Advance causal inference for agentic AI • Develop frameworks to measure how AI agents behave, learn, and affect customer outcomes. •Build causal attribution methods tailored for complex, AI-driven systems with feedback loops. •Partner with product and AI teams to define the roadmap for trustworthy agentic AI measurement. 4. Deliver high‑impact scientific work •Identify quasi-experimental opportunities and deliver analyses that drive clear business decisions. •Shape the Decision Science roadmap with a focus on scalable capabilities and material business value. •Anticipate emerging measurement needs -- especially those created by AI-driven experiences -- and build solutions ahead of demand. Qualifications * Bachelor’s degree in Statistics, Economics, or a related quantitative field. Master’s or PhD preferred. • 5+ years applying statistical and econometric methods to real decision-making contexts. • Deep expertise in causal inference (e.g., synthetic controls, RDD, IV, DiD, and modern causal ML). • Experience designing or scaling experimentation systems, frameworks, and best practices. • Ability to bring clarity and direction to highly ambiguous problems. • Strong communication skills and executive presence. • Proficiency in SQL and Python or R. Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: #J-18808-Ljbffr