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Quantitative Researcher


LOCATION: 190 S. LaSalle St. Suite 1950, Chicago, IL 60603 - ON-SITE


Analyze financial market data to identify patterns and potential opportunities.  Research and develop alpha signals.  Build predictive and profitable trading models for market-making and inventory management.  Manage the lifecycle of the research process, from idea generation, data analysis and management, and prototyping to the back testing of predictive models.  Measure ex-ante strategy performance and impacts of strategy parameter changes.  Conduct research to improve risk management, market coverage, and to meet compliance/regulatory requirements.  Ability to present both summarized and detailed research results along with resulting recommended action items.  Option to Telecommute.


This position requires a Master’s Degree, or foreign equivalent, in Physics, Mathematics, Information Technology, or a related field plus 2 years of experience in quantitative research or related field.  Additionally, the applicant must have professional experience with: 1) Programing SQL, R, Python, C#, C++ and kdb (Q) to analyze trade and market data and offer insight into strategy algorithm performance, back test algorithms, suggest optimal algo parameters, provide ideas for new algorithms; 2) Monetizing statistical patterns across global securities and future markets; 3) Navigating internal data warehouses or UNIX/Linux OS servers to pull internal data/logs and utilize various data engineering skills to extract, transform, translate, and clean binary and numerical data from multiple sources for detailed analysis; 4) Maintaining portfolio performance, developing model/algo monitoring processes/reports to ensure stability of targeted portfolio performance, and maintaining key competency of the models over time; 5) Developing and maintaining data visualization and surveillance tools to ensure internal processes and methodologies are compliant with current and evolving regulatory requirements; 6) Exploring statistical patterns across large pool of internal and external data, documenting, and communicating the potential opportunity of monetizing the patterns within global markets; and 7) Designing, developing and validating the models using statistical or machine learning techniques, including multivariate time series models, supervised learning, and Monte Carlo simulation, to capture the proposed value proposition.

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