Theorem uses Data Science to invest in the best Marketplace Lending loans


COMPANY SIZE Between 10 and 25

# OF ENGINEERS Less than 5


TAGS Big Data, Finance, Machine Learning, YC Winter 2014


What do we do?

We are a cross-disciplinary team applying machine learning and rigorous scientific investigation to revamp the lending and securitization space. This is one of finance’s least sexy areas, but is a multi-trillion dollar market- and it’s where the financial crisis started. Bad technology was a major cause, and even after almost 10 years, no one has fixed it.

Technical challenges

1) We’re exploring ideas across multiple disciplines: machine learning, biostatistics, survival analysis and epidemiology.
2) We build complex numerical systems, which require significant research prior to implementation.
3) Our systems are full stack, and need to be fast.
4) We interface with several lending companies and work with large amounts of data.
5) We value correctness, maintainability, elegance, and testability of code. We want to do things the right way over just getting things “done.” We’re strict about our code style and quality so that you don’t have to spend your time tracking down other peoples’ bugs.

Why join us?

1) We have invested over $300mm for our clients, and are meaningfully profitable.
2) We'll likely never need to raise capital again, which means no dilution.
3) Building good financial forecasting models is extremely challenging from both a technology and research point of view.
4) We offer an academic work environment with a focus on research
5) A large number of our clients are non-profits and university endowments

Our Founders

Abeer Agrawal


Hugh Edmundson


Our tech stack

  • Pandas
  • Scikit Learn
  • Python
  • Statistics
  • Fast Code
  • Numpy
  • Data Science
  • C++
  • Machine Learning

Our investors

  • SV Angel
  • Max Levchin
  • Two Sigma
  • Data Collective
  • Accel Partners
  • Initialized Capital
  • Sam Altman