State-of-the-art approaches to mortgage data and modeling
Who We Are
We are a Fintech startup working at the intersection of Cloud Computing, Big Data and Machine Learning to provide state-of-the-art approaches to querying and modeling mortgage market data. We are founded by partners with decades of senior-executive-level experience at major investment banks. Conveniently located in Newark right next to the train station.
To deliver dramatic improvements in the speed, flexibility and cost of performing mortgage market analytics and thus open new possibilities for business and market intelligence.
Our flagship cloud-native application provides a flexible and intuitive interface to the several billion-plus row mortgage data sets released by Fannie Mae, Freddie Mac, and Ginnie Mae with a sub-second response time for typical queries.
Our Data Platform facilitates the delivery of mortgage data sets into computational notebooks (Jupyter, R Notebook). We are also in the process of developing software libraries that are tailored for mortgage analytics and model building.
Our unique combination of domain knowledge in the mortgage/housing markets and technology expertise equips us to tackle the most complex problems and delivered tailored solutions.
Deep Industry Knowledge
The three senior partners of MachineSP possess deep expertise in all facets of the Securitized Products market having each held senior management roles at various top-tier financial institutions including banks, hedge funds, and a GSE.
They have a proven track record of successfully implementing multi-year technology projects in the context of large financial institutions while remaining attentive to their business needs and organizational complexities.
Years of Experience
The three senior partners average more than 20 years of financial sector experience spanning a wide range of asset classes (Mortgages, Rates, Corporates, Equities, Volatility) and roles (Portfolio Management, Trading, Risk Management, Research, Technology, Quantitative modeling).
In-house Technology and Research Teams
We make extensive use of open source tools to take advantage of the latest advances, backed up with thorough research to optimize efficiency and performance of each use case.
Our technology platform has been developed to allow us to perform empirical research and also quickly build econometric and machine learning models from multiple datasets.