- Londres, Angleterre, Royaume-Uni
- CDI, Plein-temps
- Credit Suisse -
- 22 juin 18
Global Markets (Equities) AES Quantitative Analyst, ANL/ASO, London, #107920
AES is the flagship product in the firm's electronic trading franchise, which is used to execute orders on behalf of clients in markets around the globe. The role offers an unrivalled opportunity to learn about global electronic trading, working for the industry-recognized market leader in Algorithmic Trading. The successful candidate will work very closely with the trading desk and be directly involved in a challenging and high-profile area of the finance industry. The role will also entail quantitative work across the broader European Cash Equities platform.
- Focus on intraday equity markets and research behind enhancements towards execution algorithms.
- Primary responsibility will be for fundamental research to enhance existing and explore new trading algorithms
- Involvement across the entire research and implementation process - from idea generation and backtesting to overseeing implementation, performance reviews and ongoing calibration
- Further work may include more bespoke and/or customised reporting and analysis and working on targeted improvements
- Tech development will be required with automation and repeatability a focus, though exact responsibilities will depend on the candidate and their experience/expertise
- Primarily an agency execution focus rather than a proprietary trading focus
Scientific academic background (physics/maths/engineering/etc, PhD preferred but will consider Masters with relevant Thesis topic)
Significant Plusses include :
- Experience and demonstrated research in Intraday Equity Markets using High Frequency Tick data.
- Programming (especially Matlab, but also Java, C#, C++, R, Python, etc) and demonstrated ability to automate repetitive tasks and research
- Experience with automated agency trading systems a significant plus
- A track record of relevant research in intraday equity markets e.g. at PhD or Post Doc levels
- Some experience in a quantitative role in a major financial institution is a plus
- Experience with time series / machine learning, especially where there have been financial applications of this research
- Experience analysing and making concrete suggestions to improve execution performance, and experience proving / demonstrating these improvements
- Visualisation tools (e.g. Tableau)
- Ability to perform under pressure, both on individual and team assignments
- Good communication skills, written and conversational, and ability to defend own research and theory to peers