Theorem (YC W14) Is Hiring Machine Learning Experts and Researchers
We are a cross-disciplinary team applying machine learning, software engineering 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.
We are a full-stack startup, not just another vendor; we make tools so that we can use them. We have capital commitments of over $100mn dollars for a set of institutions and individuals. We’ve raised a multimillion dollar seed round from top tier investors including major VCs, Max Levchin, SV Angel and Two Sigma.
Our problems are very technical: we are creating unique ML algorithms, or implementing versions that have never been used in industry before. We work with (or want to work with) approaches that span everything from traditional models like logistic regression, to SVMs, tree models and boosting, amongst many others.
We are munging data from a variety of sources, and applying advanced feature selection and engineering, as well as dimensionality reduction techniques.
We try to be very rigorous with our investigations. We have a comprehensive model validation mechanism including a backtester. We care more about analysis being correct then achieving a p-value below .05.
We operate in a competitive environment, and need our code to be fast: our production models produce decisions in under a hundred milliseconds.
Interested? Email us at jobs@theoremlp.com
MACHINE LEARNING SOFTWARE ENGINEER
What you bring to the table:
* Professional experience applying machine learning techniques and building production ML systems. You should know a few ML methods well, and be ready to learn a lot more!
* Coding skill in Python, C++ or similar. We currently use Python, but welcome developers of any background, as long as you can pick up Python. Experience with numpy/scipy/pandas is a big plus.
* Experience in writing fast, performant code (especially numerical code) is a big plus
* Experience doing repeatable research in any scientific field is a plus
* 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.
* Knowledge of quantitative finance is cool – but in no way a requirement.
What you’ll do:
* We’re exploring ideas from across computer science and statistics, including supervised learning, imbalanced data and anomaly detection, ensemble learning deep learning and boosting.
* You’ll develop and implement solutions for real world, large-scale machine learning/statistical problems.
* You’ll build and maintain our software exploratory data analysis tools, model validation system, backtester and best-in-class execution system.
MACHINE LEARNING RESEARCHER
What you bring to the table:
* We’re not just applying algorithms from existing libraries, we want a machine learning savant who can understand our business problems and contribute to our analysis on a deeper level.
* Experience with feature engineering and mining external data to help boost model performance
* Research background devising novel algorithms and applying them to real world data to achieve robust, repeatable results.
* Familiarity with supervised-learning methods and modern statistics
* Knowledge of quantitative finance is cool – but in no way a requirement.
What you’ll do:
* The market is our lab. You’ll investigate data, construct a hypothesis, and instead of writing a paper, actually apply your ideas and make money!
* You’ll be extremely rigorous about causation: care more about analysis being correct then achieving a p-value below .05
* We’re exploring ideas from across computer science and statistics, including supervised learning, natural language processing, imbalanced data and anomaly detection, deep learning, time series, feature extraction and selection, and many, many other areas.
* We are very interested in ideas from biostatistics, survival analysis and epidemiology, so applied experience in these areas would be cool.