Meet the Team

James has extensive experience in the Energy industry in various global roles with a focus on identifying and capturing business value through the application of data science and business intelligence. James holds a Bachelor’s degree in Supply Chain Management from the University of Houston with a concentration in applied mathematics.

Rebecca holds a bachelor’s degree of information and media from the University of Technology Sydney, and is undertaking her post graduate diploma in mathematics and statistics from the University of Southern Queensland. She has a background in writing for tech publications.

Vicky previously analysed data, specialising in causal inference, for a small research consultancy in London and worked on projects for governments, international organisations and companies such as the UN and Siemens. Vicky has a BA in Human Sciences from Oxford University and an MSc in Decision Science.

Margaux is a data scientist at McKinsey & Company, Sydney. Margaux holds a Bachelor’s in Computer Science and Mathematics from Télécom Paristech (“Grande Ecole”), and a Master’s in Statistics from Imperial College, London.

Parvez has an extensive background in computer vision, machine learning, image processing and signal processing. He possesses several years of experience in working with multi-disciplinary cross-functional teams and translating real-world problems into a mathematical setting. He is experienced in diverse application domains such as medical imaging, bioinformatics, neuroscience, multimedia delivery, remote sensing and camera networks.
Parvez obtained his Ph.D. in Electrical Engineering and Computer Science from University of California at Berkeley.

Melissa is a Data Scientist at Uber. Before Uber, Melissa worked as data scientist at Square supporting the product team with her own machine learning models. Melissa got a B.S in Computer Science and Mathematics with a minor in Economics from Duke University. Her thesis research was in computational game theory, applying game theoretic models to prevent cheating in casinos.

Pavel has extensive skills in machine learning, data mining, and distributed systems. Formerly a principal data scientist at Microsoft, Pavel is familiar with web content management algorithms and real-time content analysis. Pavel obtained his Ph.D. in Computer Science from Cornell University.

Elissa’s interests include using smart grid big data, predictive analytics, and machine learning to create data-driven solutions to energy efficiency and electrical grid management. She currently works as the senior software engineer with OhmConnect. Elissa received her M.S. in Energy Tech. Engineering from Stanford University and her B.S. in Civil and Environmental Engineering from Carnegie Mellon University.

Julie is a senior data scientist at Textio, with expertise in machine learning and statistical experimentation. She has applied and honed her skills on projects ranging from sensor-based activity recognition, to marketing response prediction models, to deduplication and ranking algorithms for business listings. Prior to joining Microsoft in 2010, she earned her undergraduate degree at Stanford University and her PhD at the University of Washington, both in computer science. Julie is passionate about teaching and about outreach and mentorship to groups that are underrepresented in computing careers.

Yuan has extensive statistical modeling experience in linear models, logistic models, time series models, decision trees, model regularization, and cross validation. Yuan has a PhD in Economics from the University of California at Davis.

Ike has spent time consulting with fortune 500 companies and startups and has helped them discover and extract actionable insights from their data to drive business decisions. He has worked on a variety of problems from algorithmic trading to auction price prediction and using machine learning for intent resolution. Ike holds a Masters in Industrial & Systems Engineering and Bachelors degree in Physics and Electrical Engineering. He is excited about the possibilities that abound with the proliferation of data.

John is constantly amazed by the amounts of data available around us and thinks about how to squeeze the maximum amount of meaning from those data. He also thinks about how to offload as much work as possible to machines without losing accuracy. John holds a BS in Electrical Engineering and Computer Science and a BA in Cognitive Science from UC Berkeley. John worked on automatic computer virus classification using machine learning and collaborative filtering using social networks.