Job Location: 100 Avenue of the Americas, New York, NY 10013
Note: Company “Hybrid” work attendance policy: In-office work attendance required at the aforementioned office address for collaboration days based on each team’s requirement; telecommuting / working from home is permissible for remainder of the same month.
Duties: Apply quantitative financial analysis/statistical analysis/data analysis skills, including estimation methods, time series analysis, and machine learning methods to research, formulate, design, and develop sophisticated predictive quantitative financial investment models and financial trading strategies to trade futures, fixed income instruments and their derivatives in a variety of global markets. Research, design, and develop production-quality, high-reliability, highly tuned numerical quantitative code using complex financial linear algebra, statistical modeling, and numerical optimization techniques. Analyze requirements to determine feasibility of quantitative financial trading strategy design. Apply scientific analysis and mathematical modeling to predict and measure outcome and consequences of financial strategy design. Originate new trading ideas and quantitative models with knowledge of financial market empirical anomalies through latest research findings in quantitative finance literature. Utilize financial analysis, mathematical/statistical analysis, and predictive quantitative financial modeling/financial engineering skills to research, analyze, develop, and execute on data-driven solutions to financial investment problems. Generate hypothesis and design customized financial research metrics to analyze market impact and construct mathematical/statistical models for prediction and evaluation. Generate investment ideas and conduct quantitative financial analysis to support all aspects of the investment, portfolio management, and risk optimization process, including providing research on forecast models, risk estimation, and portfolio optimization.
Minimum education and experience required: Master’s degree or equivalent in Financial Engineering, Statistics, Mathematics in Finance, Applied Physics, Computer Science, or related field plus 3 years of experience in Quantitative Research, or related experience; OR. PhD degree or equivalent in Financial Engineering, Statistics, Mathematics in Finance, Applied Physics, Computer Science, or related field.
Skills required: Must have demonstrated knowledge of the following quantitative research and analysis skills: quantitative finance and financial markets; modern portfolio theories (including portfolio optimization and portfolio construction) and risk modeling. Must have experience with statistical analysis of time series data, panel data, and cross-sectional data. Must have experience working in fixed income markets. Must have experience with analyzing large-scale financial datasets; linear algebra, probability, statistics, machine learning, and convex optimization. Must have experience programming with Python, Matlab, and database tools (SQL). Must have experience with utilizing machine learning libraries and frameworks (e.g., pytorch, scikit-learn). Must have experience with conducting rigorous independent scientific research. Must have experience with designing and implementing back-testing frameworks. Must have experience with data cleaning, preprocessing, and transformation. Must have experience in portfolio risk management, factor analysis, and risk estimation. Must pass company’s required skills assessment. Employer will accept any amount of experience with the required skills.
Rate of pay: The base pay for this role will be between $165,000 and $325,000 per year.This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
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