Big Data in the Tech Industry


Data science is rapidly growing and evolving. A host of industries now rely on massive datasets containing millions or even billions of observations, such as large-scale retailing, telecommunications, and internet social media. And, once these datasets are compiled, it is the data scientists’ job to uncover hidden patterns, unknown correlations, and other information that the company can use to make better decisions. In this panel, we will hear from data scientists in the tech industry, and we will learn how industry practitioners developed the skills to approach and analyze big data’s problems.


Monica Lee, Data Scientist, Facebook (PhD Sociology 2014)

Data scientist, PhD. sociologist & software engineer leveraging very big data to research political behavior, cultural tastes/beliefs, and social networks. Currently working on product development and ads targeting for Politics & Civic Engagement/2016 U.S. Elections at Facebook. Skilled in machine learning, predictive modeling, map/reduce, Python, R, NLP, stats, graphs, visualization. LinkedIn

Chenfei Lu, Data Scientist, Uber (PhD Booth 2016)


Anna Bertiger, Data Scientist II, Microsoft (AB 2006, PhD Cornell Math 2015)

Anna is an applied scientist in a product group at Microsoft working on developing machine learning models for making sense of the data in the Microsoft ecosystem. Her team works on translating innovative ideas in computer science, mathematics and social science into products and insights that will be useful to Microsoft. Before Microsoft she had a brief stint in a quantitative analysis group at National Grid and was a postdoc in the Department of Combinatorics and Optimization in the Faculty of Mathematics at the University of Waterloo with research interests in algebraic combinatorics and combinatorial algebraic geometry. LinkedIn 

Jonathan Keane, Data Scientist, Mattersight (PhD Linguistics 2014, Postdoc 2016)

Jonathan Keane is a Chicago-based data scientist and linguist by training. After earning a PhD in linguistics at University of Chicago, he was a 2016 fellow with Data Science for Social Good also at the University of Chicago. Currently he is working as a data scientist at Mattersight, developing reproducible, scalable predictive models, pipelines, and other data-driven resources. LinkedIn