Machine Learning Researcher
Imagine discovering something undiscovered
G-Research is a leading quantitative research and technology company. By using the latest scientific techniques, we produce world-beating predictive research and build advanced technology to analyse the world’s data.
We use the latest scientific techniques and advanced data analysis methods to discover the undiscovered. Our researchers are free to explore ideas, finding patterns in large, noisy and real-world data sets to predict the movements in global financial markets.
Our mission is to develop models to forecast financial time series. This is a challenging and highly competitive space so rather than deploy standard methods off the shelf you will likely need to extend classical methods or develop entirely new techniques. Our problems are well-defined and success is highly measurable and has direct impact on the business.
You will have access to vast amounts of data (both structured and unstructured), large computing resources and a world class research platform. You will apply 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. We read the latest publications in the field and discuss them within the firm’s vibrant research community. We attend the leading conferences worldwide (e.g. NIPS, ICML, ACL etc.).
This is a pure research role where you will have the freedom to develop and test your ideas with real-world data in an environment that resembles academia: you are only limited by your imagination!
Who are we looking for?
The ideal candidate will at 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 NIPS, ICML, ICLR etc. is highly desirable.
Why should you apply?
- Highly competitive compensation plus annual discretionary bonus
- 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
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