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: Researching, developing, and back‐testing quantitative execution planning financial investment models by using financial economics, financial analysis methods, and quantitative statistical analysis techniques. Keeping up with literature in quantitative finance, asset pricing, market microstructure by reading journal articles and attending academic talks and conferences. Using various financial and statistical tools to discover, clean, organize, and manipulate raw data sets for the purpose of further financial analysis. Applying data mining, pattern recognition, and statistical analysis methods to research, design, and develop sophisticated quantitative statistics-based financial modeling systems. Researching, analyzing, developing, and implementing financial investment strategies and ideas based on financial data. Researching, identifying, analyzing, and assessing financial investment strategies using systematic approaches and quantitative, mathematics/statistics-based computational methods. Researching and analyze financial investment portfolio performance, run simulations, and conduct research to improve investment fund performance. Researching, analyzing, developing, and implementing sophisticated portfolio construction methodologies, from improving predictive risk models formulation, creating robust alpha forecasts, to analyzing impacts among multiple portfolios. Using sophisticated quantitative, statistics-based financial models to run scenario analyses to test performance improvements and risk mitigates for portfolios.
Minimum education and experience required: Master’s degree or equivalent in Finance, Business Administration, Computer Science, Mathematics, Statistics, or related field. Position does not require specific years of experience but requires listed skills.
Skills required: Must have demonstrated knowledge of market microstructure and financial markets, including detailed understanding of exchanges, over-the-counter markets, price discovery, high-frequency trading, price impact and transaction costs. Must have demonstrated knowledge of asset pricing, including factor models both theoretical and empirically and understanding of mathematical probability, including continuous time stochastic processes, as they relate to asset pricing. Must have demonstrated knowledge of statistical models for analyzing large-scale data sets and conducting inference on data sets including linear models, generalized linear models, time series modelling. Must have demonstrated knowledge of linear and non-linear forecasting techniques applied to return forecasting. Must have demonstrated knowledge of producing high-reliability, highly tuned mathematics‐based numerical code and use knowledge of statistical learning to test financial research hypothesis and perform quantitative financial research and analysis. Must have demonstrated knowledge of programming skills in languages used for statistical inference, such as Stata, Python or R, and counterfactual analyses, such as Matlab or Mathematica. Must have demonstrated knowledge of obtaining, cleaning, and transforming data and ability to conduct exploratory analysis. Must have demonstrated knowledge of working with large and high-frequency data sets. Must have demonstrated knowledge of writing production-level Java, Python code. Must pass company’s required skills assessment. Employer will accept any amount of graduate coursework, graduate research experience or experience with the required skills.
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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