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Machine Learning Researcher (Hedge Fund, Neural Nets, DL/RL)

Job Type:
Web Developer
Salary Description:
£80000 - £150000 per annum + bonuses

Machine Learning Researcher (Hedge Fund, Neural Nets, DL/RL)

We are looking for a Machine Learning Researcher / Quantitative ML Analyst (Hedge Fund, Neural Nets, DL/RL) to join our dedicated Machine Learning Research team; helping us to create some of the most advanced predictive Machine Learning models in the world and implement them within the financial markets.

Culturally, we would describe ourselves as a startup within a larger organisation. The way we operate is very similar to academia, so as a Machine Learning Researcher / Quantitative ML Analyst you will be asked to go away and read the latest publications within the fields of Deep Learning / Reinforcement Learning / Bayesian Inference / Gaussian Process / Times Series Forecasting and look at new and innovative ways you could implement this within our business. As a Machine Learning Researcher / Quantitative ML Analyst if you enjoyed the style in which you conducted your postdoctoral research or your PhD - though felt a frustration with no ‘real-world impact’ - this could be a good move for you. Our fund manages nearing thirty billion dollars; a growing number year on year.

What we can offer a Machine Learning Researcher / Quantitative ML Analyst

* A place in a leading hedge fund working with the some of the brightest minds in the ML-for-finance industry
* Non-hierarchical structure, startup culture, hack rooms + music rooms
* To be a part of something bigger with the ML community; we host ML meetups attending by leading academics / industry leaders
* A chance to publish your own papers
* To join a dedicated team of 7 ML Researchers, as we grow to ten people in 2018.

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

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