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Eka Finance

Systematic Machine Learning Quant Researcher

Eka Finance London, United Kingdom
Posted 1 month ago In-Office Permanent High Base+ Bonus
T
Posted by
Tina Kaul
Recruiter
Leading quant fund are looking to hire a machine learning quant researcher onto their systematic team. You can be based in London or Geneva or New York.

Role:-

 

Conduct quantitative research with PM and other quants to develop and back-test different machine learning and statistical models, as well as productionize such models.

 

Combine sound financial insights and machine learning techniques to explore, analyze, and harness a large variety of datasets.

 

             Use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave.

 

             Apply machine learning to a vast array of datasets

 

 

 

 

Requirements:-

 

 

PhD degree in Computer Science/Engineering or Statistics with specialization in Machine Learning

 

 

             Experience working with large datasets and machine learning techniques

 

 

Experience in one or more of deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametrics, NLP or approximate inference.

Publications at top conferences such as NeurIPS, ICML, ICLR etc. is highly desirable.

 

             Experience in a high performance language (ideally C++, or similar languages)

 

            Outstanding performance in any quantitative field or contest (Kaggle, hackathons, olympiads, academic contests etc).

            Experience implementing machine learning algorithms in industry.

 

          They are open to ML quants who are already working within finance or ML quants within tech who are interested to move to finance.

 

 

 

Apply:-

 

Please send a PDF CV to quants@ekafinance.com

ABOUT COMPANY
London, United Kingdom
HR & Recruitment
Eka Finance is a leading global quantitative finance recruitment consultancy in the banking and finance industry. We offer front office recruitment so...
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