Jennifer Farver
Jennifer Farver

Adjunct Professor of Data Analytics

Print Overview

Jennifer Farver is the CTO at Popular Pays, a content platform for brands and agencies that connects brands and agencies to content creators.  She participates in the local and national tech community, speaking at events such as the SaaStr Annual conference and the Grace Hopper Celebration of Women in Computing.

Previously, she was the vice president of engineering at Civis Analytics, a data analytics consultancy and software product company where she lead product development (engineering, design and product management) for Civis' data analytics platform and software products.  Prior to joining Civis, Farver was a software developer at Boston-based Ab Initio Software, an industry pioneer in parallel enterprise ETL (extract-transform-load) environments.

Farver believes that computational literacy is an essential and incredibly useful component of a modern education and enjoys sharing with students the delight of programming.

She has previously taught courses in Artificial Intelligence and Database Systems at the University of Virginia, where she was on the faculty in the department of Systems and Information Engineering. She earned her BS in Civil Engineering from UC Berkeley.  At MIT, she earned a master's and PhD in transportation network optimization.


Print Vita
Ph.D., 2005, Center for Transportation and Logistics, Massachusetts Institute of Technology
M.S., 2001, Center for Transportation and Logistics, Massachusetts Institute of Technology
B.S., 1998, Civil Engineering, University of California, Berkeley

Academic Positions
Assistant Professor, Department of Systems and Information Engineering, University of Virginia, 2004-2006

Other Professional Experience
Director of Engineering, Civis Analystics, 2014-present
Developer, Ab Initio Software, 2006-2010

Print Research

Print Teaching
Full-Time / Evening & Weekend MBA
Programming for Analytics (KMCI-935-0)
Analytics systems are software systems. In this course, students will gain a working familiarity with programming for analytics using the industry’s dominant languages, tools and libraries. Students will also learn what engineers know about building software that is reliable and accurate, integrates multiple technologies, scales and performs well, and adapts to changing requirements. Topics will include: typical idioms and design patterns, designing for scalability and change, testing, and performance tuning. Through rigorous examples, students will build the perspective necessary to work with technical teams and avoid costly engineering failures. Course exercises will be conducted primarily in the Python programming language.