Data science + design thinking: A perfect blend to achieve the best user experience
Who is this presentation for?
- Developers, data scientists, machine learning engineers, AI engineers, and AI researchers
- Executives and strategic decision makers with responsibilities in technology, marketing, finance, research and development, and general management
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 and 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 amazing user experience. Design thinking is a methodology for creative problem solving developed at the Stanford d.school and 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 prepares a recipe for applying design thinking to the development of AI/ML products. You’ll discover deep customer empathy and fall in love with the customer’s problem (not the team’s solution), and you’ll learn to go broad and narrow, focusing on what matters most to customers. Michael shows you how to get customers involved in the development process by running rapid experiments and quick prototypes. These lessons blending data science and design thinking can be applied to products that leverage supervised and unsupervised machine learning models, as well as “old-school” AI expert systems.
You’ll take a look at several case studies along the way. Mint users lose $250 million in overdraft fees every year. Using the data from Mint’s 10 million users, Intuit applied a machine learning algorithm that predicts if you’re likely, within three days, to have an overdraft. Mint alerts you in time, with helpful suggestions on how to avoid the exorbitant insufficient funds fee. QuickBooks Self-Employed has an ML model and UX that allows automatic categorization of whether trips are business or personal to accurately rack up potential tax deductions. TurboTax’s Tax Knowledge Engine uses advanced AI to translate more than 80,000 pages of US tax requirements and instructions into a software oracle that can explain computations to DIY tax filers so they have greater confidence in the calculations in their returns, and can maybe save some of the 7 billion hours Americans spend collectively filing taxes every year.
What you'll learn
- Discover deep customer empathy for the customer’s problem (not the team’s solution)
- Learn to go broad and narrow, focusing on what matters most to customers and how to get customers involved in the development process by running rapid experiments and quick prototypes
Michael Radwin is the vice president of data science at Intuit with responsibility for leading a team dedicated to using artificial intelligence and machine learning models for security, antifraud, and risk. Previously, Michael used machine learning ensemble methods to fight online advertising fraud as the vice president of engineering with Anchor Intelligence, and he built ad-targeting and personalization algorithms with neural networks and Naive Bayesian classifiers and scaled web platform technologies, Apache, and PHP as director of engineering with Yahoo. He holds an ScB in computer science from Brown University.
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