Job Responsibilities (including, but not limited to, the following):
- Building algorithmic, computer-driven models
- Conducting research on academic quantitative finance literature
- Applying an alignment of innovative methods in Applied Mathematics, Computer Science, and Financial Economics
- Partnering with team members to build and improve our infrastructure and tools for trading,
risk management, and attribution
- Extracting and analyzing large amounts of historical data from a variety of structured and
unstructured sources
- Designing and testing new predictive signals, data sets, or trading strategies
- Building machine learning systems used to predict patterns in asset returns, risks, trading costs, or other aspects relevant to managing our portfolios
What are you like?
- 2-5 years of experience, demonstrated programming proficiency, particularly in R and/or Python
- Bachelor’s or advanced degree in Operational Research, Computer Science, Statistics,
Mathematics, Engineering, or a similar discipline
- An independent thinker who can build creative approaches to complex problems and articulate those ideas clearly through verbal, written, and visual media
- Strong quantitative, analytical, and programming skills; preferably demonstrated by real-world research projects and/or code repositories
- Experience with databases and query languages preferred
- Passion for financial markets, investing, and trading
- Familiarity with popular machine learning/deep learning/statistical packages (such as scikit-
learn, TensorFlow, PyTorch, etc.) a plus