laborny | Machine Learning Quantitative Risk Analyst in New York, NY

Machine Learning Quantitative Risk Analyst

  • Selby Jennings QRF
  • 172 W 104th St
  • New York, NY 10025
  • Full-Time
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A leading international investment bank is creating a brand-new modeling and analytics team within Model Risk responsible for designing and developing state-of-the-art models, methods, and algorithms for capital markets pricing and risk. This framework will be built using cutting edge machine & deep learning methods, and will involve the computation of prices & risk measures for derivative instruments across a variety of asset classes. The team will solve PDEs and simulate stochastic processes with pricing methods such as MC simulation, numerical integration, or finite difference. Responsibilities include driving new methodology development by conducting applied research of emerging applications used in pricing and risk modeling. These cutting edge models, methodologies, and algorithms will be applied to all areas of the bank with the goal of driving best modeling practices. Responsibilities Design and develop cutting edge models, methods, and algorithms for capital markets pricing and risk Apply state-of-the-art machine learning and deep learning techniques for applications in pricing and risk management Drive new methodology development by conducting applied research of emerging applications used in pricing risk modeling Design, implement, and automate tools & technologies within computing platforms to improve efficiency and drive effectiveness Conduct research and collaborate with the internal and external quantitative (including academic) community regarding the latest developments in machine learning/deep learning, pricing, and quantitative risk practices Requirements Master's Degree or Ph.D. in quantitative field such as Mathematics, Physics, Computational Sciences, Statistics, Engineering, or related fields At least 2+ years of direct work experience in an advanced quantitative finance, mathematics, or scientific field Programming background and experience using Python or C/C++ Excellent grasp of stochastic calculus and stochastic processes; derivatives valuation a plus Strong mathematical, analytical, and computational skills Fundamental quantitative and conceptual problem solving skills, ability to think independently, and desire to be part of a brand new cutting edge platform at one of the leading investment banks in the world Other Desired Qualifications Sound knowledge of stochastic processes (jump, jump-diffusion, diffusion), SDEs, and PDEs Experience with computing platforms and tools, including for derivatives pricing, deep learning, and machine learning Knowledge of one or more of the following approaches: numerical solution of PDEs - finite elements, numerical integration, finite differences, MC simulation of SDE, optimization, machine learning/deep learning Experience or knowledge of Interest Rates, Equity, or FX products with models including jumps, stochastic volatility, or stochastic interest rates Experience in computationally intensive applications featuring both the development and use of computational models, in particular for models and methodologies similar to pricing and risk models
A leading international investment bank is creating a brand-new modeling and analytics team within Model Risk responsible for designing and developing state-of-the-art models, methods, and algorithms for capital markets pricing and risk. This framework will be built using cutting edge machine & deep learning methods, and will involve the computation of prices & risk measures for derivative instruments across a variety of asset classes.

Associated topics: application architect, architecture, c/c++, expert, java, perl, php, programming, senior software engineer, software engineer lead