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: Research and analyze complex problems in financial markets using mathematical/quantitative computational methods, numerical algorithms, and statistical approaches; research and develop financial trading strategies based on mathematical/quantitative analysis from idea generation and data collection to analysis and model creation; apply machine learning-based quantitative analysis and research techniques and market knowledge to large, often novel or unconventional quantitative datasets; advance existing machine learning/statistics research initiatives and open opportunities to pursue previously unexplored machine learning-based quantitative research topics; research, design and develop predictive financial modeling systems using advanced quantitative modeling and mathematical/statistical analysis skills; and develop production-quality, high reliability and highly-tuned numerical code. Minimum education required: Doctoral (Ph.D.) Degree in Mathematics, Statistics, Finance, Computer Science, Computer Engineering, Physics, Electrical Engineering, or related quantitative field. Skills required: Must have knowledge of the following quantitative skills and technologies: Stay abreast on the latest academic research conducted in Machine Learning and Statistics and ability to conduct in-depth research projects in a highly quantitative field of science, as evidenced by publication of research work in academic journals, conference proceedings, or other academic research publications; linear algebra, mathematical probability theory, statistical hypothesis testing, statistical learning, machine learning, convex optimization, stochastic modeling, dynamic programming and stochastic optimization; ability to work with large datasets, clean and preprocess data, and perform statistical analysis; Python (including Pandas, NumPy, SciPy, Scikit-Learn, and PyTorch packages) and C++; statistical techniques including hypothesis testing, regression analysis, multivariate analysis, and understand different sampling methods and experiment design methods; coding and algorithm design, including data structure, complexity analysis and familiar with basic algorithmic paradigms such as recursive algorithms, backtracking, graph traversal, dynamic programming and sort algorithms; and ability to visualize complex data and analysis through tools including matplotlib and seaborn packages. 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