Staff Data Scientist

Overview

We're looking for a Staff Data Scientist to revolutionize the way TurboTax measures marketing.

We're a cross-functional Decision Science Team dedicated to driving business performance by empowering leaders, product managers, marketing managers, and analysts to make better decisions, identify new opportunities, and shape strategy. We tackle complex, high-stakes technical challenges using advanced quantitative methods such as experimental methods, causal inference, and machine learning.

In this role, you will be at the forefront of using statistics and econometrics to develop innovative marketing measurement solutions that will guide marketing decision-making and strategy. This is an extraordinary opportunity to make a massive impact quickly!

• A Bachelor's degree in Statistics, Economics, or a related quantitative discipline. A Master's degree or PhD is a plus.
• A minimum of 5 years of experience applying statistical and econometric skills to make decisions.
• Proficiency in SQL and a statistical programming language such as Python and/or R.
• Demonstrated expertise in causal inference, including synthetic controls, regression discontinuity, and instrumental variables-with a track record of rigorously solving problems with these methods.
• Experience in marketing measurement-including incrementality testing, media mix models (MMM), and attribution-is a plus.
• Proven ability to navigate through ambiguity and deliver results that drive business impact.
• Excellent communication skills, with the ability to collaborate effectively with both technical and non-technical colleagues.

• Design and execute incrementality tests across key marketing channels.
• Develop causal inference models to measure marketing performance.
• Identify quasi-experimental opportunities, conduct relevant analyses, communicate results to leadership, and collaborate with leadership to turn findings into actions.
• Create frameworks for integrating incrementality testing, models, and attribution to inform marketing decisions that align with business goals.
• Develop processes and systems to deliver scalable capabilities rather than one-off analyses.