Staff Data Scientist, Causal Inference

Job Overview

How should TurboTax be using causal inference and machine learning methods to make decisions across marketing, product, and business strategy? We are looking for a talented Staff Data Scientist who can lead the way in how we identify opportunities and drive major business impact with a well-rounded data science toolkit.

Being part of our cross-functional Decision Science Team means you'll be at the forefront of driving business performance. We empower our leaders, product managers, marketing managers, and analysts to make better decisions, uncover new opportunities, and shape strategy by tackling complex, high-stakes technical challenges using advanced quantitative methods, including experimental methods, causal inference, and machine learning.

As a tech lead for end-to-end causal inference and predictive modeling projects at TurboTax, you'll be instrumental in shaping our most critical decisions. This unique opportunity allows you to join as a trailblazer and redefine the application of econometrics/statistics and machine learning in a major tech company from the ground up.

Responsibilities

• Broad influence over the Decision Science Team’s agenda and roadmap that outlines how we can use causal inference and machine learning to develop capabilities that deliver hundreds of millions of dollars of business value.
• Set the gold standard for causal inference and predictive analytics at Intuit.
• Advise and mentor other economists and data scientists on scientific best-practices and on leveraging causal inference and machine learning to deliver business value.
• Identify quasi-experimental opportunities, conduct relevant analyses, communicate results to leadership, and collaborate with leadership to turn findings into actions.
• Establish processes and systems to create scalable capabilities and robust data products rather than one-off analyses.
• Anticipate future business challenges and key questions, designing methodologies, models, and solutions to address them.
• Use state-of-the-art time series and forecasting techniques to integrate micro and aggregate data, developing reliable forecasting models that adequately convey uncertainty.
• Engineer robust machine learning pipelines that can reliably power key business processes and customer-facing applications

Qualifications

• 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; equivalent experience will be considered.
• At least 5 years of experience applying statistical / econometric and modeling skills in decision making.
• Demonstrated expertise in causal inference—including but not limited to 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
• Strong track record of applying cutting-edge econometric methods within a fast-paced, dynamic environment.
• 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.
• Proficiency in SQL and a statistical programming language such as Python and/or R.