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: Research, design and test predictive financial modeling systems by using advanced quantitative modeling and statistical analysis skills in the application of computational and analytical principles. Utilize quantitative analysis/research and data science practices to research and formulate testable hypotheses about equities markets. Derive data-driven hypothesis about co-movement behaviors of equity price and return predictability across similar companies. Apply statistical analysis and quantitative techniques including machine learning, regression, and neural-network-based methods, to research, analyze, formulate and create sophisticated quantitative financial trading strategies. Use knowledge of probability and statistical learning to perform quantitative statistical predictive analysis. Develop production-quality, high-reliability, and highly-tuned statistics-based algorithms for data processing, data visualization, and statistical inference. Read academic papers in the field of statistical learning and machine learning and apply advanced techniques in research. Build tools to improve the research process of quantitative researchers across the company. Write detailed reports on quantitative research and tools for company’s management.
Minimum requirements: PhD Degree in Statistics, Applied Mathematics, or related quantitative field.
Alternative minimum requirements: Master’s Degree in Statistics, Applied Mathematics, or related quantitative field plus 3 years of experience in Quantitative Research and Analysis types of position(s).
Skills required: Must have knowledge of the following quantitative skills and technologies: Ability to develop and fine-tune statistical models; ability to perform statistical data analysis on large-scale data sets, including global equities market data, trading data, and alternative data sources; Time series methods to analyze market price and return data and perform statistical tests about stationarity and mean-reversion behaviors on time series data; Regression analysis and statistical inference to understand correlations, tackle multicollinearity, and analyze power of predictive features; Machine learning and deep learning techniques including tree-based methods and neural-network-based methods to explore non-linear relationships between predictive features and market returns; Statistical modeling principles and practices, including hypothesis formulation and testing, application of various statistical modeling techniques, model selection and validation via in-out-sample method, and model diagnostics; ability to write code and develop efficient algorithms to implement large-scale data analyses, computations, and simulations; R and Python; data analysis and visualization libraries, including Pandas, NumPy, and Matplotlib; and Machine learning libraries, including Scikit-learn, TensorFlow, and Keras. Must also pass company’s required skills assessment.
Base salary: 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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