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. You will also participate in the research and execution infrastructure build up and report directly to the Head of Quant Analysis.
You will have spent at least 4- 6 years experience within finance in a quantitative capacity within cash equity.
Any prior experience of working on equity risk premia strategies will be very useful and they will consider candidates working on CRB desks who are extremely quantitative.
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.
Experience of coding in Python is essential.
Educated to PhD level.
Please send a PDF resume to email@example.com
Internal Number: 6006080
About Eka Finance
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