Summary: In 2012, the Harvard Business Review named Data Scientist the sexiest job of the 21st century. The article and subsequent studies have called out a shortage of data scientists that will increase over the next 20-30 years. In this talk, I will explore what exactly is meant by the term “data scientist” and describe some aspects of STEM training that can transform a person into a data scientist in the eyes of corporate employers. Using my own career transition from a mathematician to data scientist, I will cite examples of problems that data scientists study including energy usage and renewables integration, environmental monitoring, market intelligence, and hiring and retention practices. I will pay particular attention to the “secondary” skills of STEM workers that can help them succeed as data scientists in the corporate world.
Presenter Bio: Dr. Genetha Anne Gray is an analytics research scientist in the Data Center Group at Intel where she works on the design and develop AI algorithms. Previously she was part of the Talent Intelligence & Analytics organization where she analyzed talent supply chains, studied career progression, and modeled the changing representation of women and URMs in the workforce. Before joining Intel in 2014, Genetha spent 12 years as a member of the technical staff at Sandia National Labs in Livermore, CA. There, she worked on problems related to the electrical and mechanical engineering of systems, the storage of nuclear waste, groundwater remediation, cyber security, and energy including renewables integration and grid operations. She has a Ph.D. in Computational & Applied Mathematics from Rice University and specializes in analytics techniques for decision making under uncertainty including optimization, data fusion, model validation, and uncertainty quantification. She has co-authored more than 25 research publications and given more than 50 presentations. Genetha also co-authored a recent text book on environmental modeling.
Register here: https://www.meetup.com/Sacramento-Women-in-Data-Science/events/237809624/