Machine Learning Researcher / Quantitative ML Scientist


Premium Job From Understanding Recruitment

Recruiter

Understanding Recruitment

Listed on

10th September 2018

Location

London

Salary/Rate

£125000 - £130000

Type

Permanent

Start Date

ASAP

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Machine Learning Researcher / Quantitative ML Scientist

We are looking for a Machine Learning Researcher (Time Series / Mathematical Modeling) to join our world leading research company; helping us to predict the future of financial markets.

If you enjoyed the style of working in academia but also like the idea of working for one of the largest research companies in the world, this could be the right step for you. As a Machine Learning Researcher, you would be working on a variety of different areas like Neural Networks, Deep Learning, Bayesian statistics, approximate inference, Time-Series forecasting and probabilistic models. You would be working in a small team of Machine Learning Researchers on dedicated projects enjoying flexible working hours and a lot of freedom regarding you research.

What we can offer a Machine Learning Researcher (Time Series / Mathematical Modeling)

* The chance to work for a leading research company

* Flexible working hours, the freedom to work on your projects the way you prefer, a great company culture and exceptional team spirit

* The chance to work on exciting real-world problems within Machine Learning

* A chance to visit the leading conferences in Machine Learning (NIPS, ICML) and the opportunity to publish your own papers

* Being a part of a small team of Machine Learning Researchers with the chance to enjoy an exceptional working environment including team events, team building activities and a dedicated open working space

* Relocation package and visa sponsorship supplied for the right candidate

Key Skills: Machine Learning Researcher, Quantitative Researcher, ML, Artificial Intelligence, AI, Finance, Neural Networks, Python, Gaussian Process, Bayesian Inference, Time-Series, Multi-Agent Systems, Deep Learning, Reinforcement Learning, Probabilistic Models, Approximate Inference, Neural Networks, Natural Language Processing, Markov Models, Signal Processing, Computational Statistics, Econometrics, Mathematical Modelling

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