Staff Data Scientist

OverviewHow 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.What you'll bringBroad 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 valueSet the gold standard for causal inference and predictive analytics at IntuitAdvise and mentor other economists and data scientists on scientific best-practices and on leveraging causal inference and machine learning to deliver business valueIdentify quasi-experimental opportunities, conduct relevant analyses, communicate results to leadership, and collaborate with leadership to turn findings into actionsEstablish processes and systems to create scalable capabilities and robust data products rather than one-off analysesAnticipate future business challenges and key questions, designing methodologies, models, and solutions to address themUse state-of-the-art time series and forecasting techniques to integrate micro and aggregate data, developing reliable forecasting models that adequately convey uncertaintyEngineer robust machine learning pipelines that can reliably power key business processes and customer-facing applicationsHow you will leadA 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 5 years of experience applying statistical / econometric and modeling skills in decision makingDemonstrated 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 methodsApplied experience leveraging machine learning—including but not limited to predictive forecasting, explainable ML, and end-to-end model pipeline development—to drive meaningful business impactStrong track record of applying cutting-edge econometric methods within a fast-paced, dynamic environmentA demonstrated ability to navigate through ambiguity and deliver results that significantly impact the businessExcellent communication skills and the ability to work effectively with both technical and non-technical colleaguesProficiency in SQL and a statistical programming language such as Python and/or R

#J-18808-Ljbffr