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, formulate, develop, back-test, and compare predictive quantitative financial models using advanced mathematical and statistical modeling skills. Apply data mining, pattern recognition, machine learning, optimization, linear algebra and statistical analysis including generalized regression models, time series models and NLP models to extract trading signals from extremely noisy and unstructured data. Formulate robust algorithms for model calibration based on large historical data sets and solve challenging mathematical problems encountered in the financial investment trading process. Design comprehensive experiments and metrics to perform robust model comparisons using knowledge of probability and statistics. Develop production-quality, high reliability, highly tuned numerical program code to implement models and test research ideas.
Minimum education and experience required: PhD degree or the equivalent in Computer Science, Electrical Engineering, Mathematics, Statistics, Physics, or related field. Position does not require specific years of experience but requires listed skills.
Skills required: Must have demonstrated knowledge with statistical data analysis on large datasets including regression analysis, time series analysis, deep learning, and machine learning methods for building predictive models. Must have demonstrated knowledge with NLP models. Must have demonstrated knowledge with statistical algorithms for handling latent variables or embeddings. Must have demonstrated knowledge with the rigorous design of experiments for method comparison and model sensitivity/robustness analysis via simulations. Must have demonstrated knowledge with model generalization via transfer learning. Must have demonstrated knowledge with complex time series models. Must have demonstrated knowledge with Python, R, SQL, and the Linux operating system. Must have demonstrated knowledge in writing production quality code. Must have demonstrated knowledge in probability, statistics, and linear algebra. Must have demonstrated knowledge in presenting and publishing technical details in formal venues. Must pass company’s required skills assessment. Employer will accept any amount of graduate coursework, graduate research experience or 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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