Job Description

As a machine learning trading analyst at CFL, you’ll be challenged from the outset to collaboratively have real impact on the firm’s performance through the application of our financial modeling tools and solutions in an immediate and significant way. You will be provided with an environment to both prove and continually improve your machine learning and analytical skills in the competitive and far-reaching world of finance, while enjoying a quality of life in the Austin, TX area unmatched by nearly any place offering a comparative opportunity. You will use your analysis skills to solve problems that help reveal the nature of how 21st century financial markets work in the age of machine learning and AI and become part of a 21st century, scientifically-driven, elite fund. You will help research, design and implement solutions around your findings in both the financial modeling and trading areas of our firm closely interacting with our software development, data-analysis, and ops teams. You will have the opportunity to grow into a role of one of our quarterbacks helping glue together parts of our team to implement our financial models and solutions at the highest level of excellence.

We are an early-stage start-up, enjoying the advantages of being a small but mobile, flat and elite team able to quickly implement some of the most exciting new technologies while leveraging our team’s past experience gained through years spent deep in the structure of the financial markets. We are looking for entrepreneurial-minded individuals who thrive off solving real-world problems using a true scientific approach, in a collaborative environment, who prefer the freedom to execute and exercise their skills on top priorities, and who demonstrate a self-oriented “can-do” attitude.


Compensation: Negotiable

Job Qualifications:
- Excellent written and verbal communication skills.
- Driven and self-orienting. Our team is comprised of individuals who tend to self-prioritize and self-orient to challenges and solve them.
- Proven track record of self-directed or self-motivated scientific and/or data-based projects.
- Collaborative attitude. Someone who understands the nature of how one plus one can equal far more than two.
- Familiarity with standard tooling: git, Docker, github/bitbucket.
- Comfort with cloud computing services AWS or Google Cloud.
- Python Expertise.
- Understanding of data structures and algorithms.
- Passion to prioritize and perform at the highest level.

Highly desired:
- Passion to implement and leverage the latest machine learning technologies to design and optimize financial models and trading tools.
- Expertise in numpy, scipy, scikit-learn packages.

Plus, but not required:
- Passion for financial markets and for how real economic information transfers worldwide.
- Experience in a real-world application research project or entrepreneurial project. 
- Big-data ETL pipeline design experience.
- Familiarity with Matlab, Java or C++.
- Kubernetes, Kubeflow, Airflow.
- Experience interacting with or deploying distributed sql & nosql databases.
- Experience interacting with distributed, GPU-based cluster architectures.