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The successful candidate should enjoy working in collaborative environment and willing to contribute in all parts of the investment process including signal and risk modelling, portfolio construction and execution.
Equity Data: the candidate should have hands-on experience in working with a variety of equity datasets including market data (e.g. Datastream, Factset), fundamental data (e.g. Worldscope, Compustat, Capital IQ) and analyst data (e.g. IBES, Bloomberg). Experience with alternative datasets (e.g. holdings, news, transcripts, supply-chain) would be beneficial.
Signal Construction: the candidate should be familiar with signal construction methodologies (e.g. statistical regression, Bayesian modelling) and have practical experience of testing and building signals at a variety of investment horizons.
Portfolio Construction: The candidate should have knowledge of optimisation techniques (e.g. quadratic and conic optimisation) and familiarity with actual applications of the methods in portfolio construction. Knowledge of commercial optimisation software (e.g. Axioma, MOSEK) would be an advantage.
Execution: The candidate should be familiar with execution strategies for quantitative equity strategies including pre and post-trade analysis, algo choice and transaction cost modelling. Experience of building execution algorithms would be beneficial.
PhD in a quantitative subject.
Strong coding ability in Python. Python is essential.
Please send a PDF resume to Tina Kaul at firstname.lastname@example.org