Senior Software Engineer

San Francisco, CA, United States


Role Location

  • San Francisco, CA, United States


11 - 25 people


436 Bryant St
San Francisco, CA, 94107, US

Tech Stack

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

Role Description

Key responsibilities: You’ll develop and implement solutions for real world, large-scale machine learning/statistical problems. You’ll build and maintain our best in class execution systems, data pipeline, backtester and model validation systems We’re exploring ideas across multiple disciplines: computer science, statistics, biostatistics, survival analysis and epidemiology including supervised learning, natural language processing, imbalanced data and anomaly detection, time series, feature extraction and selection, and many, many other areas.

Experience & Knowledge requested: Professional experience in designing systems, and with numerical or scientific computing. 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. 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 with databases and dev ops preferred. Machine Learning experience is a big plus, but not required. Experience in writing fast code (especially numerical code) is a big plus. (No, we are not a high frequency trading shop.) Experience doing research in any scientific field is a plus.

About Theorem

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.

Company Culture

Smart, friendly and kind. Hardworking, respectful. Eagerly nerdy, collaborative, research and science obsessed.

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