Data Science Salon
Description
"Presented by Ali Vanderveld, Director of Data Science at ShopRunner. ShopRunner is an e-commerce company that receives feeds of product data from over 100 different retailer partners, including large department stores and retailers that specialize in electronics, appliances, nutritional products, and more. In order to provide a great user experience on our website and in our mobile app, we need to have one easy-to-navigate product taxonomy. We also...
Description
"Forecasting is widely used in a number of business, but can it be used to optimize operations in an emergency department? This talk will walk through the development of a forecasting model to predict future arrivals to the emergency department. We will review the fundamentals of forecasting, discuss feature engineering, and how to get your first forecast off the ground."--Resource description page
Description
"Presented by Douglas Hamilton, Chief Data Scientist, NASDAQ's Machine Intelligence Lab. Over the last decade, hundreds of billions of dollars of capital have retreated from active to passive management, driven in part by investors seeking lower management fees. With this we have seen rapid growth in the index and exchange traded fund space, leading to the development of several distinct classes. At least one of these classes, Smart Beta, is ripe...
Description
"Presented by Kelsey Redman, AVP, Data Science at Comerica Bank. Purchasing 3rd party data on individuals can give great insights on customers, but first we have to know which individuals from that outside data source are actually customers and which are just prospects. Without a unique identifier like SSN or Driver's License number from the 3rd party data, we have to use a combination of name, address, and demographic information to identify the...
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"Presented by Sangeeta Krishnan, Former Director, Enterprise Data Management & Strategy at Asembia. Artificial Intelligence (AI) is the current buzz word across all industries. Marvin Minsky's definition of AI describes it as the science of making machines do things that would normally require human intelligence. However, the opinions are split across two camps. On one side we hear about new AI gadgets getting introduced in the market that would empower...
Description
"Presented by Dhivya Rajprasad, Data Scientist at Levi Strauss & Co. Levi Strauss and Co has always been at the helm of innovation with their classic denims and seasonal takes on the future of denim . We would like to enable users who visit our website, receive our emails and visit our stores to have the most personalized experience with easier product discovery. To enable this, I have built recommendation systems based on live and past user behavior...
Description
"Presented by Fatih Akici, Manager, Risk Analytics and Data Science at Populus Financial Group. As intelligent systems deepen their footprints in our daily lives, algorithmic bias becomes a more prominent problem in today's world. The position of executives and data science leaders to this issue is generally reactive, in that, companies solely respond to the requirements coming from regulatory agencies. In this presentation, I am going to argue why...
Description
"Presented by Chris Lindner, Manager, Product Science at Indeed. Over the past decade, data science has exploded as a lucrative, high-demand career. During this time, we've seen rapid expansion in both the demand for data scientists, and for the number of individuals trying to get into the field. But what exactly does the title 'data scientist' even mean? Who are these 'data scientists, ' and where do they come from? Are we becoming overly flooded...
Description
"Presented by Amy Daali, Chair at IEEE Engineering in Medicine & Biology Society. With recent advancements in artificial intelligence, the healthcare industry is primed to benefit significantly from Machine Learning and Deep Learning technologies. This talk will answer the burning question on who is going to fight for AI in healthcare? New concepts such as 'DIY healthcare' will be explored. We will discuss key important players who are going to lead...
Description
"Presented by Joshua Malina, Senior Machine Learning Engineer at AMEX. Time series data is really fun to play with, but you have to know how to do it. In this talk, I dive into an open source data set to show you how Pandas makes time series data investigation more accessible. After this presentation, you will know about, time series decomposition, hypothesis testing and investigation, data quality issues related to time series, and resampling methods."--Resource...
Description
"Presented by Jesse Barbour, Chief Data Scientist at Q2ebanking. Due to the specialized and sophisticated nature of many commercially focused financial products offered by banks and fintechs, building recommender systems around those products is especially difficult. Taking inspiration from the field of neural language modeling, we will discuss an application of learning node embeddings on a large-scale financial transaction graph in order to solve...
Description
"Presented by Bojan Babic, Sr. Software Engineer at Groupon. Groupon is a dynamic Marketplace where we try to match millions of the deals organized in different verticals and taxonomies with the demand across 20 countries around the world. Modeling such complex relationships requires sophisticated machine learning models that utilize hundreds of user and deal features. Customers discover deals by directly entering the search query or browsing on the...
Description
"Presented by Sylvia Tran, Data Scientist at Gracenote. User preferences and content similarity are both key to recommendation systems. While content similarity has been widely explored and utilized by many companies in the media & entertainment industries, it still remains relevant as the amount of data and metadata available continues to grow and change. This talk discusses some of the challenges of content similarity and explores a few different...
Description
"Presented by Alex Schwarm, VP/Head of Data Science at Dun & Bradstreet. For many teams, the most challenging step in delivering useful results, is less about the modeling techniques and methods and more about having access to the right data with the appropriate data coverage of the domain of interest. In this talk, we will describe two specific use cases where data pays a crucial role: one around identifying supply chain risks and one related to...
Description
"Presented by Jason Dolatshahij, Director of Data Science at Stash. Stash is the digital platform for saving & investing that promises financial inclusion for all. When we launched our checking account at the end of last year, we already had millions of customers, but we were about to make first contact with a formidable new foe: bank fraud. As a data science team, how do you develop and deploy a model when the dimensions of the problem are brand...
Description
"Presented by Moody Hadi, Group Manager, Financial Engineering at S & P Global Market Intelligence. Counterparty financial statements, particularly for small and medium enterprises can be difficult to handle. Financial analysts need to be able to distill out relevant line items in order to calculate their credit exposure to a counterparty for lending purposes. The solution solves a labor intensive, expert driven inefficient process and frees up the...
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"Presented by Anupama Joshi, Director, AI Engineering at Target. Companies are moving towards AI/Machine learning very fast. Data scientist are building models and training models. But challenges come when deploying models in production. How to maintain multiple models? Creating a common platform that allows model management and deployment easily and reliably is becoming a necessity for organizations to accelerate product development. In this talk,...
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"Presented by Shilpi Bhattacharyya, Data Scientist at IBM. Who does not love the American television sitcom - Friends? And we definitely want to learn what makes this sitcom so popular. Can the most important aspects of some of the top shows of all the times be related? Is there something common which makes them a success? If not, can we find out and draw a correlation amongst them? In this talk, I would demonstrate the essential elements of few of...
Description
"Presented by Raktim Saha - Director, Digital Insights, CGI. The focus of this talk is to showcase how Machine learning and AI is leveraged to radically outperform traditional loan-underwriting acceptance process for one of the largest lenders in US by utilizing a broader set of market, demographic, and various events data. Implementing an automated data-driven process in a large Enterprise and especially in a highly regulated industry has its own...