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Play a key role in supporting the trading & structuring teams across our Index Trading activity.
Our Index Trading business is currently recruiting for a Master graduate or newly qualified (or soon to be qualified) PhD level candidate who can support the sales, structuring and trading teams.
You will be engaged in a wide range of activities including:
researching literature on investment factors (Value, Momentum, Congestion) across different asset classes
implementing models using state-of-the-art methods from econometrics / machine learning
developing a portfolio of systematic trading strategies
You will be also be responsible for the development of quantitative analysis tools for use by the trading desk as well as general trading and execution platform improvement.
We are looking for knowledge of quantitative finance and relevant qualifications, either Masters degree or PhD level. You will also need hands on experience with big data (class or project with Machine Learning) and Python programming skills.
If you have demonstrative passion for financial markets and trading and strong theoretical understanding of derivatives, please apply now.
Find out more about Macquarie at www.macquarie.com/about
Macquarie understands the importance of diversity and inclusion - our long history of success has come from being different. At Macquarie we value the innovation and creativity that diversity of thought brings. The one thing we all have in common is our focus on high performance. If you're capable, motivated and can deliver, we want you on our team.
Macquarie is an equal opportunities employer and does not discriminate on the grounds of age, disability, sex, sexual orientation, gender reassignment, gender identity, marriage, civil partnership, pregnancy, maternity, race (including colour and ethnic or national origins), religion or belief.
We facilitate a range of flexible working arrangements within our teams. Talk to us about what flexibility may be available.
Advertised: 14 Nov 2019 GMT Standard Time Applications close: Categories: Junior, Quantitative Analytics