Jenna Gustafson
About Jenna Gustafson
Jenna Gustafson is a Data Scientist at CivicScience, where she has worked since 2019. She holds a BS in Chemical Engineering from the University at Buffalo and a PhD in Chemical Engineering from the University of Pittsburgh, specializing in computational modeling for gas sensor development.
Work at CivicScience
Jenna Gustafson has been employed as a Data Scientist at CivicScience since 2019. In this role, she utilizes her expertise in scientific research, data modeling, and problem-solving to support various teams within the organization. Her work involves analyzing data to derive insights that inform business strategies and decision-making processes.
Education and Expertise
Jenna Gustafson holds a Doctor of Philosophy (Ph.D.) in Chemical Engineering from the University of Pittsburgh, which she completed in 2019. She also earned a Bachelor of Science (BS) in Chemical Engineering from the University at Buffalo in May 2014. Her academic background includes specialized training in computational modeling for gas sensor development, which contributes to her analytical skills in data science.
Background
Jenna Gustafson began her academic journey at Shenendehowa High School before pursuing higher education. She attended the University at Buffalo from 2010 to 2014, where she studied Chemical Engineering. Following her undergraduate studies, she continued her education at the University of Pittsburgh, where she engaged in research as a Graduate Student Researcher and later as a Ph.D. Candidate.
Previous Research Experience
Before her current role, Jenna Gustafson gained extensive research experience at the University of Pittsburgh. She worked as a Graduate Student Researcher from 2014 to 2019 and briefly as a Doctoral Researcher in 2019. Additionally, she served as an Undergraduate Researcher at Stony Brook University in 2013. Her research focused on computational modeling, enhancing her skills in data analysis and application.
Career Transition to Data Science
Jenna Gustafson transitioned into the field of Data Science without a predefined career path, identifying as an 'accidental Data Scientist.' This transition reflects her adaptability and willingness to apply her engineering background in new and evolving fields, allowing her to leverage her skills in data analysis and research in a practical setting.