JuniorQuantitativeAnalyst

WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. 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 s