Executive Job Description | Proprietory Trading group of an international Investment Bank and also an aggressively expanding Hedge Fund seek Quantitative Analysts/Traders for Low, Mid and High-Frequency Proprietary Equity (Cash, Derivatives) Trading/Statistical Arbitrage. Candidates for Mid and High Frequency Trading positions should be experienced in the development of statistical arbitrage trading strategies in the US, Europe or Japan and typically come from either customer or proprietary trading groups in a bank or hedge fund. Requirements: Experience with development of one or more of the medium/high frequency statistical arbitrage trading strategies (e.g. mean reversion, analyst revisions, volume momentum, pairs, etc.). Experience with statistical methods and time series modeling methods (various regression methods, hypothesis testing, ANOVA, ARIMA, etc.). Experience formally evaluating/backtesting trading strategies. Experience working with the Thomson-First Call data set a plus Experience working with traditional equity risk management methods (mean/variance optimization, factor analysis, etc.). Experience using machine learning techniques (neural nets, GA's, hidden markov models, etc.). Experience working hands-on with non-linear constrained optimization methods on high dimensional problems. Experience in the analysis and modeling of high frequency equity (tick) data. Strong academic credentials (degree from a leading university, PhD preferred, MS considered, in Mathematics, Statistics, Physics, Economics, Econometrics, Finance or Computer Science; publication record helpful.) Candidates for Low Frequency Trading positions should have experience in the analysis of trading/investment opportunities based on economic fundamentals/fundamental data, and typically work either as a Quantitative Analyst or Portfolio Manager in the Capital Management industry or in the quantitative side of equity research. Requirements: Experience with statistical methods (regression, hypothesis testing, etc.). Understanding of economic fundamentals that effect equity valuations. Experience working with the Compustat data set a plus Experience working with traditional Equity Risk Management methods (mean/variance optimization, factor analysis, etc.). Experience formally evaluating and backtesting trading and investment strategies. Experience working with statistical analysis tools such as Splus, Gauss, SAS or SPSS. Exceptional educational background: advanced degrees from leading universities in computational sciences, with preference for majors in Math, Statistics, Physics, Economics/Finance; math/physics, etc. competitions participations, publications records are all plusses (pragmatically, it is understood that lack of some of the academic credentials may be compensated by Quantitative Research/Trading experience in Investment Management industry) |
