About the Role
Cambridge Quantum Computing is looking to hire a Machine Learning Scientist for its Machine Learning unit. The role involves applying state-of-the-art Machine Learning and specifically, Deep Learning techniques to financial time-series forecasting on diverse sources of data. The successful candidate will join the London office and will be working in a highly dynamic, research-focused group, teaming up with senior members of the team and reporting directly to the project head.
Responsibilities
- Stay on par with the latest research literature in the ML field
- Analyse high resolution financial data to identify statistically significant trading patterns and extract predictive features.
- Train state-of-the-art DL algorithms to learn profitable trading strategies.
- Prototype and Implement new ML ideas in CQC’s proprietary R&D software platform.
- Deploy ML/DL strategies in the most competitive financial markets.
Key Requirements
- A master’s degree in Machine Learning or Computational Statistics from a top-tier University, or a degree in a quantitative discipline (Math, Physics, Computer Science) and relevant experience.
- Proficiency with Python 3 and its scientific/ML libraries such as Numpy, Pandas, Scikit-Learn, Tensorflow and Keras.
- Knowledge of Deep Learning fundamentals applied to a relevant domain (Computer Vision, Speech Recognition, Natural Language Processing etc.).
- A Passion for approaching complex problems with the goal to design and deliver novel practical solutions.
Desirable Skills
- A PhD in relevant discipline and a track record of scientific publications.
- Hands-on experience in the development and deployment of ML systems gained in a commercial or research environment.
- Familiarity with time-series processing and feature extraction (particle filtering, state-space models, wavelet transforms, dimensionality reduction, etc).
- Experience in collaborative software development and version-control-systems (git).
All candidates must be eligible to live and work in the UK.