QUANTITATIVE RESEARCHER

Job Description

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: Conduct research on risk models for financial portfolio optimization. Propose mathematical models of risk grounded in modern advanced statistical techniques. Fit models using large-scale data and efficient optimization methods. Use novel data sources, clean and normalize datasets, and devise algorithms to take advantage of such data in risk modeling. Evaluate models in back-testing using a variety of metrics. Implement models in production-quality, high-performance code. Monitor model performance using real-time metrics. Diagnose issues and identify weaknesses in existing models and opportunities for improvement. Develop and maintain a repository of software tools to support the research and monitoring tasks. Research and study relevant academic literature in mathematical statistics, machine learning, and data science and extract promising ideas for new model development. Communicate results to Portfolio Managers and peers. Act as an internal source of knowledge leveraging risk expertise in a variety of problems in the alpha generation and portfolio construction pipeline. Minimum requirements: PhD Degree* in Mathematics, Statistics, Data Science, Operations Research, Financial Engineering, Electrical Engineering, or related quantitative field plus required skills listed below. (*Will also accept completion of PhD requirements for graduation if degree not issued yet). Alternative minimum requirements: Master’s Degree in Mathematics, Statistics, Data Science, Operations Research, Financial Engineering, Electrical Engineering, or related quantitative field plus 3 years of experience in Quantitative Analyst type of positions and required skills listed below. NOTE: Pre-magisterial experience or post-magisterial experience is acceptable to satisfy the 3 year experience alternative minimum requirement. Skills required: Must have knowledge of the following quantitative skills/technologies: mathematical skills, including linear algebra (including matrix factorization, spectral decomposition, and singular value decomposition), probability theory (including limit theorems and heavy-tailed distributions), and optimization (including constrained convex optimization); high-dimensional and robust statistics skills, including hypothesis testing, confidence sets, covariance matrix estimation, time series analysis, Markov models, panel data, linear and non-linear regression, Bayesian estimation, nonparametric methods, and dimensionality reduction techniques (including PCA, robust PCA, and factor models); ability to work with large-scale datasets, including data cleaning, outlier detection, exploratory data analysis, and clustering; and numerical programming skills, including the Python language (including NumPy, pandas, and scikit-learn) and parallel computing for analysis of large datasets. 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. #LI-DNI