WattzOn has a smart, hard-working and innovative team.
We're strong on machine learning, good software development practices and pleasing the user.
Our offices are in Mountain View, CA.
Founder & CEO, Ph.D.
As CEO, Martha is responsible for the company's vision, strategy, and leadership. Martha previously founded Glaze Creek Partners, which she sold to Navigant Consulting (NYSE: NCI), where she became a Managing Director. She has also been CEO of Vocomo Software, Chief Economist of PLX Systems, as well as Vice President at Analysis Group Economics.
Martha is the author of several books; Value Sweep (Harvard Business School Press), Real Options (written with Nalin Kulatilaka, Harvard Business School Press), and Case Studies in Corporate Finance (McGraw-Hill). She holds a Ph.D. in Applied Economics from MIT, and degrees in Mathematics and Economics from the University of Washington.
Martha is on the Executive Board of MIT’s Sloan School of Management, a Senior Fellow at the Milken Institute, and the board of Growth Sector, a non-profit focused on workforce development. She holds two patents.
Vice President, Engineering & Senior Data Scientist
As Vice President of Engineering, Sandra leads WattzOn's software development team, ensuring rapid iteration and releases via the agile software development method. Previously Sandra was VP of Engineering at a number of startups, and has run her own online shopping website, DealCloset, which profitably leveraged SEO based on a deep understanding of Google's algorithms and values. She has also been in engineering management at AT&T Bell Labs, Aurigin and AT&T Labs.
As Senior Data Scientist, Sandra invented WattzOn's Mr. Bill product, which applies machine learning to produce structured data from text trapped in PDFs, scans and images. She has also led development of Mr. Bill's elastic machine learning ensemble pipeline. Other work includes developing WattzOn's library of machine learning algorithms to exploit smart meter, solar, thermostat, and other highly-granular data. Her models are used to predict energy use, and to identify key habits in the role of energy use and energy savings.