Ideal Prediction creates value through combining academic engineering with a practical understanding of trading details. Trading is our firm's DNA so we understand your problems and goals and efficiently create solutions.
John founded Ideal Prediction to bring practical data science solutions to financial markets. His 20 years of trading experience is balanced: large banks and small HFTs, managing sell-side desks and proprietary trading groups, voice and fully-automated execution, and across many liquid FICC products. John has an S.B. and an M.Eng from MIT in EECS and an M.S. in mathematics from NYU. His free time is focused on family in addition to serving on the board of Broadway Technology.
Jason Mather is the head of data science. Previously Jason held quantitative sales, and trading and research roles at BNP Paribas and Citibank. He has worked across securitized products, cross currency interest rate derivatives, and equity derivatives. In every role, Jason has always focused on building tools to make his team more efficient and help clients understand their options. Jason has a B.S. in Applied Math and a B.S in Industrial & Systems Engineering, both with highest honors, from Georgia Tech.
Peter Sand has a B.S. in Computer Science from CMU and a Ph.D. in Computer Science from MIT. His research focused on computer vision and machine learning. He has many years of experience software development in a variety of different domains, including machine learning, robotics, and trading.