Jan Schiffman, Software and Data Engineering Veteran, Joins Riffyn as VP of Engineering
Riffyn Partners with Westwood and Wilshire to Fill Vice President of Engineering Role
This press release was originally published on the News page at Westwood & Wilshire.
Riffyn is a cloud-based, SaaS and Solutions provider of scientific and manufacturing process design and data analytics to the biopharmaceutical, industrial biotech, and materials industries.
On a mission to make machine learning in science an everyday reality by defragmenting scientific data and processes, Riffyn has hired Jan Schiffman as VP of Engineering. Jan joins Riffyn from TIBCO, where he served as Vice President Engineering, Data Science Products. He studied Computer Science at Rutgers University and has a distinguished career of more than 20 years of engineering and IT leadership in VP and CTO roles at Intersoft, Lehman Brothers, Monolith Software, Jaspersoft, Joscience and TIBCO, as well as founding his own start up, Predictium.
Tim Gardner, Founder/CEO, Riffyn:
"We are delighted and honored to welcome Jan Shiffman to the Riffyn team and mission. His extraordinary depth and breadth of experience in creating cutting edge technologies and leading high-performing engineering organizations will help Riffyn leap forward in its growth as an enterprise SaaS provider to life science and chemical R&D and manufacturing organizations."
The search was successfully led by Andrea Tegstam for Westwood & Wilshire’s Advanced Technology practice.
About Westwood & Wilshire
At Westwood & Wilshire, we recognize that people are an organization's top asset. We are passionate about delivering premier talent to the world’s most innovative companies. We are particularly focused on companies ranging from VC-backed start-ups to mid-sized commercial entities and our assignments range from C-Suite to mid-management. Our firm has offices in Los Angeles, Silicon Valley, and Seattle, serving clients on a local, national and global basis.
Riffyn’s pioneering cloud system, Riffyn Nexus, delivers 2X faster development cycles and 4X productivity gains to the world’s scientific R&D organizations. Its process-centric experiment design, data capture, and data analytics overcome the limitations of traditional scientific data systems — delivering results that are always ready for machine learning.