Fitchett, of Boulder, Colo., works at Transamerica, a life insurance company. Her visit to IUP is sponsored by the Society for Industrial and Applied Mathematics (SIAM).
She will present “What could I do in industry? A handful of statistics and mathematics projects where science and business drive the questions” on Sept. 9 from 6:30 to 7:30 p.m. in Room 225 of IUP’s Humanities and Social Sciences building. It is free and open to the community.
Her program will discuss the many opportunities in industry for students with strong statistical and mathematical backgrounds who are not necessarily mathematics majors.
This talk will share a handful of projects that have a statistical or mathematical component and are driven by science or business needs.
The projects she will discuss: tracking the effects of landfill contaminants on fish over time; detecting anomalies in “pattern or life” transportation routes using imagery from synthetic aperture radar; estimating the extent of music piracy among an internet provider’s customers; and modeling the spread of avian influenza in Southeast Asia.
Fitchett earned her Ph.D. in mathematics from the University of Nebraska and a Master of Science degree in statistics from Colorado State University. Following a postdoctoral position, she spent 12 years as a faculty member at Florida Atlantic University’s Honors College and at the University of Northern Colorado.
She has worked as a statistician in industry for the past seven years, first at Sandia National Laboratories, and later at Neptune, a small environmental consulting firm.
In her current position as a data scientist at Trans-america, she builds statistical models that leverage corporate and external data to facilitate underwriting of life insurance policies
In addition to her evening lecture, there is an open session to meet Fitchett from 10 to 11 a.m. in Stright Hall 205 and a presentation from 3:30 to 4:30 p.m. in Stright Hall 226-229 on data science, machine learning and some examples from life insurance underwriting.
Familiarity with math and statistics is recommended for those attending this program, which will discuss the popularity of machine learning models and the data science positions for those who build them.
This talk will start with a general discussion of machine learning, then focus on some specific machine learning models under development for life insurance underwriting.
She also will discuss how one or two models work at a high level, some of the challenges in building useful models, and the safeguards that are in place to prevent unintended negative consequences, and data science as a career path.