Modeling the Success of Software Developer Job Applications

Keynote Talk Description

As the Chief Data Scientist of a New York-centric digital recruitment platform, Jon Krohn has unusual access to the attributes hiring managers are responsive to. Jon will cover:

• the features that lead to successful applications for software engineering roles, as suggested by thousands of statistically-modeled cases
• the most in-demand data science skills, both hard and soft, and their relationship to compensation
• advice from experts on interview preparation
• the utility of open-source data sets and ideas for obtaining insights from them efficiently
• the world-improving impact machine learning practitioners can have

About the Speaker

Jon is the Chief Data Scientist at untapt, a venture capital-backed digital platform that automates the job-seeking process for technically-skilled candidates like data scientists and software engineers. By employing natural language processing, supervised and unsupervised learning algorithms, untapt optimizes the identification of fulfilling career opportunities. Jon earned his doctorate in neuroscience at Oxford University, where he published peer-reviewed papers on the use of computational statistics, including machine learning, to analyze the large-scale data of genetic and brain imaging research. Prior to joining untapt, Jon used algorithms to trade commodities at a Singaporean hedge fund as well as to serve digital advertisements at North America’s largest media agency.

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