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 computer science/computational methods, numerical algorithms, and statistical approaches. Research and develop computer and information science-based financial trading strategies based on mathematical/quantitative analysis, from idea generation and data collection to analysis and computer model creation. Collect, clean, and analyze large financial datasets and apply statistical/mathematical modeling techniques, including regression, statistical, and machine learning methods and time-series methods to research, design, and develop sophisticated quantitative, statistics/mathematics-based financial modeling systems. Use computer systems to apply mathematics-based quantitative analysis and research techniques and market knowledge to large, often novel or unconventional quantitative datasets. Use computer systems to advance existing mathematical/quantitative research initiatives and open opportunities to pursue previously unexplored financial mathematics-based quantitative research topics. Research, design, and develop/engineer predictive financial modeling systems using advanced quantitative modeling and mathematical/statistical analysis skills. Use computer systems to develop/engineer production-quality, high-reliability, and highly tuned numerical code.

Minimum education required: PhD Degree in Mathematics, Applied Mathematics, Statistics, or Computer Science.

Skills required: Must have knowledge of the following quantitative skills and technologies: linear algebra, mathematical probability theory, real analysis, statistical hypothesis testing, machine learning, deep learning, statistical reinforcement learning, signal processing, stochastic processes, mathematical optimization, and dynamic programming; quantitative modeling and statistical data analysis on large datasets; ability to apply statistical methods and machine learning techniques including deep neural networks and Bayesian nonparametric learning methods to large real-world datasets; C++ and Python, including the use of Pandas, NumPy, SciPy, Scikit-Learn, Matplotlib, Cuda, PyTorch, and TensorFlow; distributed programming, GPU programming, algorithms, and numerical techniques; ability to adapt both theory and algorithms to new applications; and publication of research work in academic journals and/or presentation at academic conferences in the field of machine learning and artificial intelligence. 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.