Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY
Diane Chang

Diane Chang
Distinguished Data Scientist, Intuit

| Attendee Directory Profile

Diane is a distinguished data scientist at Intuit, where she powers the prosperity of consumers and small businesses with machine learning, behavioral analysis, and risk prediction. Previously, Diane was at TurboTax, looking at the effectiveness of its digital marketing campaigns, understanding user behavior in the product, and analyzing how customers get help when they need it; helped launch QuickBooks Capital, predicting outcomes for loan applicants; worked for a small mathematical consulting firm and a startup in the online advertising space; and was a stay-at-home mom for six years. She applies AI and ML techniques to security, risk, and fraud. Diane earned her PhD in operations research from Stanford.


9:00am–5:00pm Tuesday, 09/27/2016
Location: 1 E 14
Alistair Croll (Solve For Interesting), Juan Huerta (Goldman Sachs Consumer Lending Group), Robert Passarella (Alpha Features), Giannina Segnini (Journalism School, Columbia University), Mar Cabra (International Consortium of Investigative Journalists), Anand Sanwal (CB Insights), Michael Casey (MIT Media Lab), Diane Chang (Intuit), Jeff McMillan (Morgan Stanley), Tanvi Singh (Credit Suisse), Kelley Yohe (Swift Capital), Michelle Bonat (Data Simply), Susan Woodward (Sand Hill Econometrics), Robert Passarella (Alpha Features)
Average rating: ****.
(4.00, 14 ratings)
Finance is information. From analyzing risk and detecting fraud to predicting payments and improving customer experience, data technologies are transforming the financial industry. And we're diving deep into this change with a new day of data-meets-finance talks, tailored for Strata + Hadoop World events in the world's financial hubs. Read more.
11:00am–11:30am Tuesday, 09/27/2016
Location: 1 E 14 Level: Beginner
Diane Chang (Intuit)
Average rating: ***..
(3.25, 4 ratings)
Almost 700,000 small businesses fail each year—many because they cannot secure critical financing when they need it. Banks would lend more if they could better distinguish the good risks from the bad. Diane Chang explains how a small team used big data to turn a 70% loan rejection rate into a 70% acceptance rate and solve a critical problem for small businesses. Read more.