Junior Quantitative Analyst

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.

• 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 WQs 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

• Undergrad Masters or PhD degree from a top university with a major in computer science mathematics statistics physics engineering or quantitative finance discipline
• Demonstrated ability to program in Python and/or C with a strong background in data structures and algorithms
• Working knowledge of Linux
• Strong problem-solving abilities
• Strong moral integrity and work ethic

WorldQuant is a total compensation organization where you will be eligible for a base salary discretionary performance bonus and benefits.

To provide greater transparency to candidates we share base pay ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on job function and level benchmarked against similar stage organizations. When finalizing an offer we will take into consideration an individuals experience level and the qualifications they bring to the role to formulate a competitive total compensation package.

The Base Pay Range For This Position Is $150000 USD.

At WorldQuant we are committed to providing candidates with all necessary information in compliance with pay transparency laws. If you believe any required details are missing from this job posting please notify us at and we will address your concerns promptly.

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