Intuit's Money Product Data Science Team is looking to drive growth across their consumer lending product by influencing product, marketing, and business strategies through deep analytical expertise and business acumen.
Requirements
- Demonstrated expertise in causal inference—including but not limited to advanced experimentation, synthetic controls, regression discontinuity, and instrumental variables—with a track record of rigorously solving problems with these methods.
- Applied experience leveraging machine learning—including but not limited to predictive forecasting, explainable ML, and end-to-end model pipeline development—to drive meaningful business impact
- Proficiency in SQL and a statistical programming language such as Python and/or R.
Responsibilities
- Lead causal inference and econometric analyses to understand and influence key levers of business growth with a crisp understanding of incremental impact.
- Design, implement, and analyze experiments and quasi-experiments to measure the impact of new initiatives in product and marketing.
- Develop predictive models and methodologies to uncover growth opportunities and support long-term business planning.
- Translate complex technical findings into clear, actionable insights for senior leadership, including product, finance, and marketing executives.
- Serve as a strategic thought partner to cross-functional leaders, bringing deep analytical expertise and business acumen to bear on Intuit’s most critical growth questions.
- Lead and contribute to projects that require advanced econometric modeling, causal inference, and experimental design.
- Deliver insights that guide investment decisions, optimize user journeys, and inform strategy at the highest levels.
Other
- A Bachelor's degree in Statistics, Economics, Computer Science or a related quantitative field is required. Advanced degrees, particularly a Master's or PhD in economics or statistics, are highly desirable.
- At least 2 years of experience applying statistical / econometric and modeling skills in decision making.
- A demonstrated ability to navigate through ambiguity and deliver results that significantly impact the business.
- Excellent communication skills and the ability to work effectively with both technical and non-technical colleagues.
- Leadership and Ownership: Demonstrate boundaryless leadership and extreme accountability - proactively drives outcomes across teams and leads with influence, not authority.
