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Calendar of Events

Data Science in Industry

28 January 2021
Location: Virtual

ISBIS

The webinar will start at 3:00 pm and end at 5:00 pm (GMT-05:00) Eastern Time (U.S. & Canada).

Rationale: Around the World, data storing is dramatically increasing day by day, and companies need to manage the data they gather to extract information useful to make decisions. Consequently, Data Scientist positions are spreading out in many companies. But what does a Data Scientist actually do in a company? How they actually manage and elaborate data? What kind of problems do they solve using statistics? In this webinar three Data Scientists will explain their jobs at Pinterest, Amazon, and T-Mobile. The session is free of charge and we warmly invite PhD students and young statisticians to attend.

Panelists:

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Maxine Qian – Data Scientist (Experimentation) at Pinterest

  • MSDS Alum, 2017 -- Data Science intern at Williams Sonoma
  • Master’s degree in Business and Economics from Queen’s University
  • Bachelor’s degree in Economics from Wuhan University

Title: “3 ways to improve offline experiment sensitivity”

Abstract: At Pinterest, we use offline experiments to measure the impact on metrics labeled by human evaluation. These metrics, such as search relevance and content quality, offer a direct human perspective and valuable business insights that are otherwise difficult to extract from online metrics. This webinar talks about how we use statistical methods to make offline experiments more efficient, achieving higher sensitivity and lower cost.

Jennifer Zhu – Deep Learning Scientist at Amazon

  • Previously, Staff Data Scientist at GE Digital
  • USF MSDS Alum, 2017 -- Data Science intern at Vungle
  • PhD in Biomedical Sciences from Cornell University
  • Bachelor’s degree in Biology from Peking University

Title: “Using artificial intelligence (AI) to automate clinical workflows”

Abstract: Most clinical data is unstructured data, including audio recordings, laboratory reports (PDF), insurance claims (forms), documents (image files). Using the data is time- and energy-intensive. How can we automatically extract this data, so that it may be stored in a database, or connected to an interface for use by downstream applications? In this talk, I will present the Document Understanding Solution (DUS) that allows you to use the power of AWS AI for search, document digitization, discovery, and extraction and redaction of select information.

Melanie Palmer – Senior Data Scientist at T-Mobile

  • Previously, Senior Data Scientist at NBC Universal Media
  • USF MSDS Alum, 2017 -- Data Science intern at SF 49ers
  • Bachelor’s degree in Mathematics and Economics from Gonzaga University

Title: “Reshaping Refunds with Data Science: How Text Analytics Uncovers Pain Points to Improve Customer Experience”

Abstract: Customer satisfaction is essential to success in the telecommunications industry. Refund requests drive many calls to customer service and bring attention to subpar customer experiences. A text analytics model was built to “listen” to these calls and identify common pain points that lead to refund requests. Confusion making payments on the web and mobile app was a common theme, leading to accidental and overpayments. Several new UI designs were generated that kickstarted a series of AB tests evaluating a variety of payment flows and payment options. Additionally, loopholes in policy were discovered that allowed customers to game the system and receive undue refunds. The tests, in conjunction with the text analysis, highlighted a clear path forward to change policy, reduce refunds, and improve customer experience.

Organizers:

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