As part of the Career Transitions Series, Ph.D. Mathematicians now working in industry will share their experiences in an informal question-and-answer panel. Below are bios for our wonderful panelists.
Davis completed his Ph.D under Sándor Kovács in 2006. After spending three years on the academic job market while working as an adjunct professor at Seattle University, he decided to leave academia and explore a career in law. In 2012 he received his JD from Harvard Law School, and soon thereafter joined the San Francisco office of Orrick, Herrington & Sutcliffe. For a year-and-a-half he was a member of Orrick's intellectual property litigation group, where his math background helped him make sense of complex patents. Realizing that litigation didn't suit his sensibilities, he then transitioned to Orrick's Technology Companies Group, representing startup companies and venture capital firms in financing transactions and day-to-day corporate matters--and becoming the group's primary expert on modeling complex deals in Excel.
Harish graduated from the UW Math Ph.D. program in 2017, where he worked on discrete optimization under Thomas Rothvoss. He now works as a data scientist at Palantir, a Bay-area based software company, for whom he has worked at the National Institutes of Health (NIH), in addition to projects with other customers, including an oil supermajor and United Airlines. On Sundays, he volunteers as a math instructor at the San Quentin State Prison in Marin County, CA.
Austin completed his Ph.D. in 2014 under Sara Billey. After graduating passed up a post-doc to pursue a mix of higher education and industry work. He has spent three years as faculty at Highline College, a community college South of Seattle, and two years as a researcher at 1QBit, a quantum computing company in Vancouver, BC. He also has experience working with larger companies such as DOW and Fujitsu. You might consider asking him about: Community colleges, teaching jobs, discrete optimization, data science, working for a start-ups, working in Canada, applying for random industry jobs, applying for teaching jobs, applying for NSF postdocs, making tough life choices between vastly different career options.
Chelsea graduated from the University of Washington in 2009, majoring in math and philosophy and minoring in dance. After taking a year off to work as a teacher/tutor (and take analysis), she entered the math PhD program at the University of California, Davis. Though she had intended to study combinatorics and algebra, she soon discovered machine learning and pattern recognition and decided to focus in that direction. Her dissertation was titled "Analysis and Extensions of Sparse Representations in Signal Processing," and the bulk of her work consisted of developing a cool algorithm for face recognition. She started working at Amazon as a Research Scientist in 2017. She currently works with the AWS Marketing Data Science team, where she develops machine learning models to help the company personalize and optimize their marketing campaigns.