Junior Quantitative Analyst

About the role

We seek candidates interested in being based in our Austin office to work alongside a Quantitative Portfolio Manager The ideal candidate is a motivated junior quant researcher/developer with knowledge and interest at the intersection of financial markets, machine learning, and data engineering. Key responsibilities include: Searching for, understanding, and cleaning raw datasets from WQ's data library Drawing on intuition about both finance and ML models to appropriately featurize data Carrying out controlled experiments to discern the economic value of their features and feature combinations Productionize features and models via DAG scheduler Contribute day-to-day improvements to our overall Python codebase. Attention to and genuine interest in the detail of the financial data being used is valuable - the candidate should be motivated to develop their domain expertise by engaging in what may seem to be tedious inspection and understanding of data sources in order to produce appropriate and high quality models The candidate will be mentored closely by an experienced member of our team and gain experience in understanding complicated financial data, real world forecasting models, and experience the joy of seeing their work through to production with an impact on live trading Finally, there is no limit on what can be done or on the significance of the contribution. Creative use and development of any tools which scale, automate, systematize, or improves this research process is a highly valued contribution, with an enormous space available for creativity and impact