Job Title: Machine Learning / Deep Learning Quantitative Researcher (Quant Finance)
Locations: London, Amsterdam, US, and APAC
Overview:
Offering an exciting opportunity to collaborate with world-renowned machine learning researchers and former academics on challenging, real-world problems within the field of quantitative finance.
In this role, you will build state-of-the-art algorithms and deploy them on massive datasets containing trillions of observations, receiving immediate feedback on your models and optimisations. You will be supported by a team of top-tier engineers and a cutting-edge tech stack, including a rapid-feedback platform for ML experimentation, you'll work at the frontier of financial innovation.
Key Responsibilities:
- Develop and deploy advanced machine learning and deep learning models to tackle complex problems in quantitative finance.
- Conduct large-scale data analysis and build ML/DL algorithms optimised for trillions of observations.
- Experiment with model architectures, feature transformations, and hyperparameter tuning to ensure robust, reliable inferences.
- Collaborate with both in-house researchers and leading academics to shape the future of ML/DL in finance.
- Stay active in the academic community by attending and presenting at top conferences.
- Engage in a culture of continuous learning through study groups, paper discussions, and guest speaker events.
Qualifications:
- PhD in a quantitative subject (Physics, Biostatistics, Mathematics, Statistics, Computer Science, Engineering, etc.) from a top-tier university (e.g., Oxbridge, Stanford, UC Berkeley, Ivy League).
- Strong publication record with first-author contributions in leading journals and conferences (e.g., NeurIPS, ICML, ICLR, AISTATS, NAACL, ACL, EMNLP, CVPR, ICCV, Physical Review Letters, Nature Physics, etc.).
- Proven coding expertise in Python, C++, or R.
- Deep knowledge of machine learning / deep learning / NLP techniques and frameworks, with practical experience in applying them to real-world problems.
If you think you might be a good fit, please share your resume with [email protected]