Machine Learning Quant Researcher

Anson McCade
Charing Cross, United Kingdom
8 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Compensation
£ 200K

Job location

Charing Cross, United Kingdom

Tech stack

Computer Vision
Python
Machine Learning
Natural Language Processing
Reinforcement Learning
Deep Learning
Information Technology

Job description

£150,000-200,000 GBP Discretionary end of year bonus Onsite WORKING Location: Central London, Greater London - United Kingdom Type: Permanent Machine Learning/Data Science Quantitative Researcher - London/Paris My client is a quantitative hedge fund with offices globally, focusing on systematic trading. Their Quant Researchers develop and monitor strategies covering all liquid markets, including HFT/arbitrage, statistical arbitrage, CTA, Macro and event-driven models. The firm has a mandate for Quantitative Researchers who are specialised in Machine Learning, Deep Learning, Reinforcement Learning, NLP, or Computer Vision. Successful applicants will apply these techniques to analyse datasets and identify trading opportunities, and develop them into monetizable strategies in collaboration with other researchers, developers, and traders. The Role: Researching and applying Machine Learning and other Data Science techniques to analyse datasets and identify alphas. You will work closely with

Requirements

other researchers, developers and traders on the development and implementation of these strategies, and monitor their performance over time. Quantitative Researchers collaborate with each other globally. You will share ideas and work on tools for others to use across the firm, expanding the business and building your own skills. Requirements: An academic background with degrees covering numerical fields of study, such as Computer Science, Mathematics, and Quantitative Finance, PhD level degrees are preferred but not required. Experience/knowledge of finance from academic studies, internships or professional experience. Coding proficiency in at least on language, successful candidates are typically expert users of Python, and proficient with data science libraries.

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