Quantitative Researcher - Systematic Macro

Squarepoint Capital • New York, NY

21h • $180,000 - $225,000 • Onsite

About The Position

Squarepoint Services US LLC seeks a Quantitative Researcher - Systematic Macro for its New York, New York location. On behalf of an investment management firm, this role involves advising on investment decisions by developing econometric/statistical models to analyze and forecast price movements of financial securities and related economic/policy/political conditions across countries. The position also includes formulating mathematical and simulation models of investment strategies, restrictions, alternatives, conflicting objectives, and numerical parameters for the enhancement of trading through computerized algorithms, as well as implementation of models. The role requires utilizing comprehensive knowledge of mathematical models and technologies, statistical techniques including regression analysis, machine learning, and statistical inference, and financial and computer skills to enhance investment strategies based on equities or other asset classes. Additionally, the role involves producing and implementing sophisticated analyses describing new statistical effects, assessing robustness of effects, and developing new quantitative strategies making use of such effects. Performing validation and testing of both trading simulations and critical trading applications is also a key responsibility. The role also includes building applications utilizing Python to automate daily data dependency processing for trading strategies, managing live trading automations, and performing continuous monitoring of risk related to live trading automations.

Requirements

• Minimum of a Master’s degree or foreign equivalent in any STEM (Science, Technology, Engineering, or Math) field or Economics.
• 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 Macroeconomic data and their impact on asset prices.
• At least two (2) years of employment experience with Macroeconomic Relationships.
• At least two (2) years of employment experience with Programming in Python.
• At least two (2) years of employment experience with Statistical Analysis (hypothesis testing, time series analysis, linear regression).
• At least two (2) years of employment experience with Knowledge of worldwide macroeconomic release details.
• At least two (2) years of employment experience with Model building to predict macroeconomic variables.
• At least two (2) years of employment experience with Knowledge of methods and datasets used by the Bureau of Labor Statistics (BLS) to create various macroeconomic indices.

Responsibilities

• Develop econometric/statistical models to analyze and forecast price movements of financial securities and related economic/policy/political conditions across countries.
• Formulate mathematical and simulation models of investment strategies, 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 Python to automate daily data dependency processing for trading strategies.
• Manage live trading automations and perform continuous monitoring of risk related to live trading automations.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level