Machine Learning Researcher
G-Research is Europe’s leading quantitative finance research firm. We hire the brightest minds in the world to tackle some of the biggest questions in finance. We pair this expertise with machine learning, big data, and some of the most advanced technology available to predict movements in financial markets.
Machine Learning is an integral part of G-Research’s work. We use applied ML techniques to develop successful investment management strategies; it is one of the core drivers of our overall performance and success. ML has long been a key tool at G-Research and we count among our number a range of ICML and NeurIPS published researchers.
Joining our leading ML team, you will have huge amounts of (clean) data and massive compute at your fingertips, with which you will predict the future of financial markets. Because this is a very mature prediction problem, finding the 1% of difference, working at the very cutting edge of developments, is the place where success happens. You will, in effect, be incentivised to explore the state-of-the-art.
While we start with the standard ML toolkit, to make a model work our researchers really need to understand what’s going on, not just throw an out-of-the-box solution at a dataset. This involves applying machine learning methods drawn from diverse areas such as neural networks, reinforcement learning, deep learning, non-convex optimisation, Bayesian non-parametrics, NLP and approximate inference. Unlike pure problems, our researchers get near-instantaneous feedback on their work in the form of absolute numbers: success is highly measurable and has direct impact on the business.
As a team, we read the latest publications in the field and discuss them within the firm’s vibrant research community and attend the leading conferences worldwide (e.g. NeurIPS, ICML, ACL etc.).
In this research role you will be able to develop and test your ideas with real-world data in an environment that resembles academia.
Who are we looking for?
The ideal candidate will at a minimum have experience in the following areas:
Either a post-graduate degree in machine learning or a related discipline, or commercial experience developing novel machine learning algorithms. We will also consider exceptional candidates with a proven record of success in online data science competitions (e.g. Kaggle).
Experience in one or more of deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametrics, NLP or approximate inference.
Excellent reasoning skills and mathematical ability are crucial: off the shelf methods don’t always work on our data so you will need to understand how to develop your own models.
Strong programming skills and experience working with Python, Scikit-Learn, SciPy, NumPy, Pandas and Jupyter Notebooks is desirable. Experience with object oriented programming would be beneficial.
Publications at top conferences such as NeurIPS, ICML, ICLR etc. is highly desirable.
Why should you apply?
Highly competitive compensation plus annual discretionary bonus
9% company pension contributions
Informal dress code and excellent work/life balance
Comprehensive healthcare and life assurance
25 days holiday
Contributory pension scheme
Central London office close to 5 stations and 6 tube lines