This is a unique and exciting opportunity, supported by Kingston University academics and based at Instinet, a leading global broker of equities, in London. This KTP project will develop and deploy innovative machine learning techniques for trading execution performance and monitoring.
You will have primary responsibility for the application of these techniques, to deploy machine-learned products to improve the execution performance of equity trading, and machine-learned tools to monitor this performance. You will also have responsibility for authoring reports and academic publications that describe aspects of your work, and the potential impact for Instinet and its clients.
You will be working alongside another KTP Associate, employed as a Systems Architect.
This position is a 32-month fixed-term contract.
The university partner
This KTP will be led academically by Prof James Orwell and supervised by Dr Gordon Hunter from the School of Computer Science and Mathematics school within the Faculty of SEC.
What we are looking for
The ideal candidate will have a solid understanding of:
- Theoretical principles of machine learning
- Linear algebra
- Probability and statistics
- Capability to use system output information to diagnose and fix deficiencies in implementation,
- Incorrect assumptions about data, etc and experience in testing and correcting computer programs designed for specialised operating conditions
You will have a BSc (minimum of 2:1) in a subject with significant mathematical content plus MSc in mathematics, computer science or a closely related subject. And possibly a PhD in similar subjects.
Accepted degree subjects
- computer sciences and IT
Additional job details
- Number of vacancies
- South East England
- £50,000 to £70,000
Salary depending on qualifications and experience plus training package of up to £2,000
- Contract, dates and working times
- Fixed term
How to apply
Interviews: week commencing 8 July or 15 July.
If you have any questions please email.
Click Apply to start your application now. This job will be available on Prospects until 28 June 2019
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Closing date: 28 June 2019