Technical Talks - Perspectives on Data Science

February 20, 2015

LinkedIn's engineering teams build state of the art systems and algorithms to connect the world's professionals to make them more productive and successful. Up and down the stack, from best of class distributed systems like Apache Kafka to cutting edge mobile frameworks and take-no-prisoners machine learning infrastructure, LinkedIn engineering solves some of the hardest, most rewarding problems in the industry.

But we also realize that there is a lot of interesting work happening around the industry and for many companies data science is at the cutting edge. As part of our ongoing LinkedIn SF speaker series, I'm excited to lead an evening of talks by data scientists with very different perspectives on the work they're doing in the fields of analytics, network analysis, and machine learning.

Perspectives on Data Science
Tuesday, February, 24th
6:00 pm - Food, Drinks & Networking
6:30 pm - 3x15 Minute Talks

LinkedIn SF R&D Office
505 Howard St.
San Francisco, 94110

Featured Speakers
John Rauser
A former principal engineer at Amazon, John is a current member of Pinterest data science where he wears many hats, including engineer, data miner, corporate spokesperson, and technical writer. Capable in all these areas, John is happiest working on projects that combine more than one of those roles, especially when it involves the creation of innovative applications that leverage large datasets to answer real world questions.

Elena Grewal
Elena Grewal is a Data Science Manager at Airbnb. She is leads a team of data scientists responsible for the user online experience and offline experience. The team partners with the product team to understand and optimize all parts of the product, using experimentation in a wide variety of contexts. Examples include testing the impact of different search ranking strategies on the likelihood of finding a desired listing, identity verification on incident rates, and professional photography services on booking rates. Because Airbnb is a complex ecosystem involving both online and offline interactions, traditional A/B testing must be adapted and refined. Prior to working at Airbnb Elena completed a doctorate in education at Stanford where she built predictive models of friendships in schools and modeled the impact of peers on educational outcomes.

June Andrews
June Andrews is an applied mathematician specializing in social network analysis. she has worked on the search Algorithm and Yelp and designed algorithms for computing the straucture of large networks with Professor John Hopcroft. Currently, June works on Growth at LinkedIn, where she works to understand the impact of the LinkedIn Economic Graph on both the global scale and invidiual member. She holds degrees in Applied Mathematics, Computer Scienc and Electrical Engineering from UC Berkeley and Cornell.

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