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Data Scientist
2 months ago
About Intuit
Intuit is a global financial technology company that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper.
Our Mission
We never stop working to find new, innovative ways to make that possible. Our team is dedicated to using data science and machine learning to drive business impact and deliver value to our customers.
Job Summary
We are seeking a highly skilled Data Scientist to join our Decision Science team. As a Data Scientist, you will be responsible for applying statistical and econometric methods to drive business decisions and develop capabilities that deliver hundreds of millions of dollars of business value.
Key Responsibilities
- Develop and implement causal inference and machine learning models to drive business impact
- 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
Requirements
- Bachelor's degree in Statistics, Economics, Computer Science or a related quantitative field
- 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
- 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