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Senior Staff Software Engineer at LinkedIn
My graduate research focused on applications of machine learning in multi-agent systems, particularly in e-commerce settings. This research was motivated by my participation in the Trading Agent Competition, where I designed winning autonomous agents in both supply chain management and ad auction scenarios. My Ph.D. thesis explored methods by which agents in such settings can adapt to the behavior of other agents, with a particular focus on the use of transfer learning to learn quickly from limited interaction with these agents. Since graduation I have continued this line of work in the field of display advertising with the goal of improving the experiences of both advertisers and publishers.
The University of Texas at Austin
Doctor of Philosophy (PhD), Computer Science
January 1, 2011 – Present
Staff Software Engineer - Data Mining/Data/Analysis/Machine Learning
June 1, 2015 – Present
Sunnyvale, CA
Software Engineer
October 1, 2012 – June 1, 2015
Mountain View, CA
Yahoo!
Scientist
February 1, 2011 – October 1, 2012
Sunnyvale, CA
Susquehanna International Group, LLP (SIG)
Intern
June 1, 2008 – August 1, 2008
Bala Cynwyd, PA
Cultural Fit Analysis
The candidate has worked at several prominent and innovative tech companies (Google, LinkedIn, Yahoo!), which suggests adaptability to fast-paced, high-performance environments. The diverse roles from Scientist to Staff Software Engineer indicate a broad interest and capability in both research and engineering aspects of data science. The long tenure at Google and LinkedIn suggests stability and commitment. The PhD background also points to a culture of continuous learning and deep analytical thinking. The target role 'Software Engineer Data Scientist' aligns perfectly with the candidate's career trajectory and experience.
Soft Skills & Operational Fit
The candidate's resume highlights significant contributions to complex projects at leading tech companies, suggesting strong problem-solving, analytical, and execution skills. The descriptions imply an ability to work effectively in teams and drive substantial business impact. However, without specific soft skill assessments or project details, a definitive evaluation of operational fit beyond technical contributions is limited.