Quantitative Researcher, Equity Mid-Frequency

Squarepoint Capital•New York, NY

10d•$165,000 - $170,000•Onsite

About The Position

Squarepoint Services US LLC seeks a Quantitative Researcher, Equity Mid-Frequency for its New York, New York location. This role involves formulating mathematical and simulation models of investment strategies to enhance trading through computerized algorithms. The position requires utilizing comprehensive knowledge of mathematical models, statistical techniques (including regression analysis, machine learning, and statistical inference), and financial and computer skills to improve investment strategies. The researcher will produce and implement sophisticated analyses, perform validation and testing of trading simulations and applications, and build applications using Shell and Python for data processing automation. Additionally, the role involves analyzing existing strategy behavior and implementing improvements using KDB/Q and Python, tracking market history with Excel/VBA and KDB tools, managing live trading automatons, and monitoring associated risks. The position also requires leveraging asset-class-specific experience to identify market data patterns and optimize execution costs, utilizing knowledge of market structure and statistical arbitrage to enhance and develop trading strategies. The researcher will assist senior quantitative researchers and pilot research projects across multiple teams and regions to develop new mathematical models and analytical tools for investment decision-making.

Requirements

Minimum of a Master’s degree or foreign equivalent in Statistics, Data Science, Electrical Engineering, Physics, Operations Research, Applied Mathematics, Theoretical Physics, Computer Science, Econometric or related.
2 years of experience as a Quantitative Researcher, Investment Process Associate, or related position for an investment/asset management organization.
At least two (2) years of employment experience with using quantitative and statistical modeling techniques on large datasets to generate and validate trading signals.
At least two (2) years of employment experience with programming (python and kdb/q) for systematic strategy implementation to research, backtest, and deploy automated trading strategies.
At least two (2) years of employment experience with developing hypotheses, testing ideas, and efficient iteration in a data-driven environment.
At least two (2) years of employment experience with communicating quantitative results obtained through data analysis and translate knowledge into a quantitative model.

Responsibilities

Formulate mathematical and simulation models of investment strategies, relating constants and variables, restrictions, alternatives, conflicting objectives, and numerical parameters for the enhancement of trading through computerized algorithms, as well as implementation of models.
Utilize comprehensive knowledge of mathematical models and technologies, statistical techniques including regression analysis, machine learning, and statistical inference, and financial and computer skills in order to enhance investment strategies based on equities or other asset classes.
Produce and implement sophisticated analyses describing new statistical effects, assessing robustness of effects, and developing new quantitative strategies making use of such effects.
Perform validation and testing of both trading simulations and critical trading applications.
Build applications utilizing Shell and Python to automate daily data dependency processing for trading strategies.
Utilize KDB/Q and Python to analyze existing strategy behavior and propose and implement improvements.
Utilize Excel/VBA mathematical models and KDB analysis tools to track market history of specific asset classes to evaluate future profit potentials and risk margins.
Manage live trading automatons and perform continuous monitoring of risk related to live trading automatons.
Leverage on asset-class-specific experience to find new patterns in market data and explore new methods to optimize execution costs.
Utilize extensive knowledge of market structure and statistical arbitrage to improve on existing trading strategies and develop new trading strategies.
Assist team’s senior quantitative researcher’s efforts in building, validating, releasing, and maintaining highly complex automated trading models.
Pilot research projects spanning multiple teams across multiple regions to develop new mathematical models and analytical tools for critical investment decision making.