Data Science + Design Thinking: A perfect blend to achieve the best user experience

Abstract: As data scientists, we invest much of our time on the business problem, the data, the statistics, the algorithm and the model. But we can’t afford to overlook one very important component: the customer! A great AI/ML model with a poorly designed user experience is ultimately is going to fail. The world’s best data products are born from a perfect blend of data science and an amazing user experience.

Design thinking is a methodology for creative problem solving developed at the Stanford University d.school. The methodology is used by world class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung and GE.

Michael Radwin, VP of Data Science at Intuit, will offer a recipe for how to apply design thinking to the development of AI/ML products. Your team will learn how to get deep customer empathy & fall in love with the customer’s problem (not the team’s solution). Next, you will learn to go go broad to go narrow, focusing on what matters most to customers. Finally, learn how to get customers involved in the development process by running rapid experiments and quick prototypes.

These lessons of blending data science & design thinking can be applied to products that leverage supervised and unsupervised machine learning models, as well as “old-school” AI expert systems.

Case study examples will include:
Mint users lose $250 million in overdraft fees every year. Using the data from Mint’s 10 million users, we applied a machine learning algorithm that predicts if you are likely, within three days, to have an overdraft. Mint alerts you in time, with helpful suggestions on how to avoid the exorbitant Non-Sufficient-Funds fee.
Business or personal? Mobile mileage tracking for QuickBooks Self-Employed: ML model + UX = automatic categorization of individual trips easy to accurately rack up potential tax deductions.
Americans spend 7 billion hours every year filing taxes. TurboTax’s Tax Knowledge Engine, which uses advanced AI to translate the 80,000+ pages of US tax requirements and instructions into a software oracle that can explain computations to DIY tax filers so that they have greater confidence in the calculations in their returns.

Bio: Michael Radwin is a Vice President of Data Science at Intuit, responsible for leading a team dedicated to using artificial intelligence and machine learning models for security, anti-fraud and risk. Prior to Intuit, Radwin was VP Engineering of Anchor Intelligence, which used machine learning ensemble methods to fight online advertising fraud. He also served as Director of Engineering at Yahoo!, where he built ad-targeting and personalization algorithms with neural networks and naïve Bayesian classifiers, and scaled web platform technologies Apache and PHP. Radwin holds an ScB in Computer Science from Brown University.