NOTE: This role is open in either NYC or Santa Monica, CA offices.
Company is building next-generation quantitative analysis software with consumer design principles at the center. The system is built in Go, Python, and react and designed to capture the state-of-the-world in the private markets (both historical and real-time) and yield useful financial analysis, fast. We’re making software that’s delightful to use, helping both investors make brilliant investment decisions and operators understand the financial metrics that matter. The system enabling this requires a true-multidisciplinary approach to build- from data science to high-performance distributed computing to human-centered design and research.
The data scientist has the exciting, and daunting, task of solving some of our toughest challenges with a high degree of analytical sophistication. We’re looking for team members who have mastery of basic probability theory, applied machine learning, and textual analysis, but we don’t stop there. We seek data scientists that are comfortable writing tested, scalable code to migrate ideas from the workbench to production. They’re in love with visualization, and get excited about making data beautiful and intuitively accessible to end users. They develop fluency in the language of the markets and finance, and are effective communicators who are comfortable building bridges between members of these communities and their own.
A data scientist brings data to life in their imagination, in high-quality production code, and in data products for the end-user.
- Strong foundations in machine learning and probability theory, with demonstrated success working on applied challenges
- Mastery of the basics of OOP in python
- Mastery of fundamental machine learning tools in python, specifically: pandas, scikit-learn, and numpy
- Experience implementing unit and functional testing bases in production environments
- A good eye for data visualization with experience working with both static (e.g. ggplot2, plotly) and dynamic (e.g. dash, bokeh) toolkits
- Solid written and verbal communication skills, with the ability to bridge the gap between those who do and don’t speak fluent “data”
- Bonus: Applied deep learning experience, combined with a strong grasp of theoretical fundamentals
- Bonus: Experience working with “large” (>100GB) datasets using a distributed processing framework, e.g. spark, hive, hadoop
- Bonus: Experience implementing a python workflow/scheduling framework, e.g. luigi or airflow