In our fast-paced Big Data world, the need to quickly extract and apply meaningful insights derived from terabytes of data is rapidly making the role of data scientist essential across almost every industry.
Many believe the perfect data scientist would be an expert in a wide variety of technical fields ranging from mathematics and statistics to data engineering and visualization, but would also have a solid grounding in fields like business, behavioral economics and customer behavior. It’s a tall order, and it’s still rare to find expertise in such a wide variety of disciplines in a single individual.
But as more academic programs focus on developing data scientists, many organizations are left wondering how to best position their organizations to take advantage of this emerging data sciences competency.
In this panel discussion, experts from a variety of industries will share their first-hand experiences building and deploying teams of data scientists. They’ll discuss the different approaches they have tried – what worked well and what didn’t – and share practical advice on finding and hiring data the right data scientists, building multi-faceted teams with complementary skills, positioning an organization to best take advantage of data scientists’ skills, and balancing structure, culture, and mentorship to enable data science teams to succeed.
Diane Chang is a Senior Data Scientist at Intuit. During her tenure at Intuit, Diane has worked with the Consumer Group to perform in-depth behavioral analysis of the TurboTax online customers, and has explored the business impact of both online advertising spend and utilization of customer care services. More recently she has been involved in an embedding pilot where she joined the QuickBooks Financing team to use data to improve the likelihood of a QuickBooks small business obtaining financing.
Diane has a PhD in Operations Research from Stanford and has worked for a small “mathematical” consulting firm, and a start-up in the online advertising space. Prior to joining Intuit Diane was a stay-at-home mom for 6 years.
Steven Hillion is the Chief Product Officer of Alpine Data Labs. He has been leading large engineering and analytics projects for fifteen years. Before joining Alpine, he founded the analytics group at Greenplum, leading a team of Data Scientists and also designing and developing new open-source and enterprise analytics software. Before that, he was vice president of engineering at M-Factor, Inc. (acquired by DemandTec) where he built analytical applications that became a global standard for demand modeling at companies like Coca-Cola. Earlier, at Kana Communications, Mr. Hillion led the engineering group during the two largest releases of its flagship product, which remains the engine for email support at companies like eBay and Staples. At Scopus Technology (later Siebel Systems) he co-founded development groups for Finance, Telecom and other verticals.
Mr. Hillion is originally from Guernsey, in the British Isles. He received his Ph.D. in mathematics from the University of California, Berkeley, and was a King Charles I Scholar at Oxford University.
Nick is the Director of Data Science at Rackspace. He leads their data visualization and machine learning teams focused on data products. In previous dimensions, he was the lead data scientist at one of the worlds largest global management consulting firms where a portion of his time was spent helping design and structure the companies strategy around building data science departments, the other portion was spent starting and incubating data science inside fortune 500 companies. He got his start designing hardware devices for voice controlled medical beds and then became more interested in intelligent non-living things. Later, he designed and implemented scalable backend systems for predictive modeling products as well as a few production recommender systems for large retailers. Nick holds a BS in Statistics and a BA in Computer Science from the University of Iowa.
Matthew Gee is cofounder and principal at the Impact Lab, a data-analytics company focused exclusively on developing scalable data science solutions to social-sector problems. He is also a senior research scientist at the University of Chicago’s Center for Data Science and Public Policy and a research fellow at the Urban Center for Computation and Data. Matt is the cofounder of the Eric and Wendy Schmidt Data Science for Social Good fellowship, which in its first three years has paired 126 fellows with over 40 national, state, and local government organizations and NGOs to build data-driven solutions to social problems.
Matt’s applied work focuses on combining methods and problems from the social sciences with machine-learning methods and new data sources to drive operational efficiency and individual behavior change and to implement adaptive policy interventions, with a focus on energy use, sustainable development, urban systems, and local labor market dynamics. He has lead major data science initiatives with large public-sector clients, including the World Bank, national governments and agencies (Mexico, USA), state governments (California, Illinois), and cities (San Francisco, Chicago, Memphis), as well as large nonprofit organizations and for-profit companies. Matt serves as an advisor to Code for America, DataKind, and the Chicago School of Data and is a member of the World Bank’s Partnership for Open Data. He has previously worked at the US Treasury’s Office of Energy and Environment and has founded several companies focused on analytics, energy, and finance.
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