Ethics and transparency have to go hand in hand with data scientist and business – remember before you make promises you can’t keep, machine learning, AL, NLP, etc… all require good data, communication within the team creating or designing, system compatibility, solid logical programming and MATH… It’s not just a cool buzzword and something to add to your resume or website to be deemed relevant. Bias, whether implied or intentional, affects lives, knowledge of data is important now more than ever.
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About the speakers:
Carla Gentry is an acknowledged influencer and subject-matter-expert in the field of advanced analytics and data science. During the past 20+ years, she has worked with Fortune 100 and 500 companies including but not limited to, Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil, Tandus, PBA Pharma, Disney, Deloitte, Samtec, Talent Analytics and Firestone.
Acting as a liaison between the IT department and the Executive staff, Carla is able to take huge complicated databases, decipher business needs and come back with intelligence that quantifies spending, profit and trends. Being called a data nerd is a badge of courage for this curious Mathematician/Economist because knowledge is power and companies are now acknowledging its importance.
Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners. Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves. Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.