We have immediate needs for non-finance PhD backgrounds who are interested in beginning their career in the finance / investment management industry. Candidates of interest are post graduate school entry level and those with post-PhD experience in academia or another industry ie technology or healthcare. Listed below, the first position is with a bank in NYC, and the second position is with a systematic focused quant hedge fund in NYC (both firms will hire from any region in the US). Please see below for more information.

1. Quant Associate role with an investment bank (NYC): We are seeking a PhD in particle physics or string theory for a Quantitative Analyst / Associate role in a global investment bank within their offices in NYC. This individual does not need finance experience, yet will want to work in finance within an investment bank on a quantitative strategies team as a quantitative modeler on a fixed income trading desk. Responsibilities will include the development and implementation of mathematical models for pricing and risk managing a portfolio of fixed income securitized products, including prepayment models, risk models for credit securitized products, and term structure models of interest rates for all mortgage related products.

2. Machine Learning Research role with a systematic focused quant hedge fund (NYC): For this role, we are seeking a PhD or PhD candidate in machine learning, computer science, statistics or a related field. This individual needs to be both analytical and creative, have demonstrated an ability to conduct independent research utilizing large data sets, and have a curiosity about financial markets. Responsibilities will include applying, adapting, and extending existing results in the broad field of machine learning, while conducting novel research - all aspects of machine learning include predictive modeling, clustering, time series analysis, natural language processing, and computer vision. Successful researchers manage all aspects of the research process including methodology selection, data collection and analysis, implementation and testing, prototyping, and performance evaluation.