Machine Learning Researcher
London, UK
Onsite
About the Company
Our client is a leading quantitative research and technology firm with offices in London and Dallas. Known for attracting top talent in the field, they foster a dynamic, flexible, and intellectually stimulating environment where innovative ideas are cultivated and rewarded. This hybrid role is based in the heart of Central London, at their newly opened Soho office, which also houses the company’s advanced Research Lab.
Job Description
This role involves developing models to forecast financial time series in a highly competitive and complex field. To succeed, you'll often need to go beyond standard methodologies by adapting classical techniques or designing new ones entirely. Success here is both measurable and directly impactful to the business.
In this role, you will:
Work with extensive structured and unstructured datasets, large-scale computing resources, and a world-class research platform.
Apply a wide range of machine learning methods, including neural networks, reinforcement learning, deep learning, non-convex optimization, Bayesian non-parametrics, NLP, and approximate inference.
Engage with the firm's active research community to discuss the latest industry publications and attend leading conferences (e.g., NeurIPS, ICML, ACL).
This is a research-focused role, providing an academic-like environment where you can develop and test ideas using real-world data.
Requirements
Ideal candidates should bring:
A post-graduate degree in machine learning or a related field, or equivalent commercial experience in developing innovative machine learning algorithms. Demonstrated success in data science competitions (e.g., Kaggle) is also valued.
Expertise in one or more of the following areas: deep learning, reinforcement learning, non-convex optimization, Bayesian non-parametrics, NLP, or approximate inference.
Strong mathematical reasoning and analytical skills; with a need for custom modeling due to data complexity.
Solid programming skills and familiarity with Python, Scikit-Learn, SciPy, NumPy, Pandas, and Jupyter Notebooks. Experience with object-oriented programming is beneficial.
Publications in top conferences (e.g., NeurIPS, ICML, ICLR) are highly desirable.