We are looking for a Research Scientist – Deep Learning to join us to work on machine learning innovations for fighting financial crime.
You will have the opportunity to work on advanced ML techniques such as recurrent neural networks, transformers, self-supervised learning, deep generative modelling and federated learning.
Check out some of the team’s past projects here: https://www.featurespace.com/research-and-innovation/
As a Research Scientist, you will help us by prototyping and productionising ML innovations. Our small team provides a collaborative environment where knowledge exchange and software engineering best practices are valued. You will get the chance to deliver real impact and see your work protect people across the world from financial crime.
We are happy to accommodate a range of working styles, as long as you are happy to commute to our Cambridge office at least once a week for in-person meetings. Please speak to us if you require more flexibility!
Day to Day
- Interface with stakeholders within the business to understand analytical requirements and opportunities
- Research new machine learning algorithms and statistical techniques to solve problems in the detection and prevention of financial crime, with a special focus on deep learning
- Contribute to the productionisation of new analytical features through prototyping, requirements setting and implementation
- Provide research input into future analytical strategies and product development
- Participate in the planning and review processes for work in the Deep Learning team
- Share knowledge of analytical techniques and tooling across delivery and engineering teams
- Create and deliver patents, publications and external talks as appropriate
- Academic degree in a mathematical discipline (e.g. Mathematics, Statistics, Computer Science, Physics, Engineering)
- Familiarity with neural networks at a mathematical level
- Familiarity with Python, Java, C++ or another high-level programming language
- Familiarity with coding standards and best practices (e.g. clean code)
Great to haves:
- PhD or post-doctoral research experience in a relevant discipline
- Publications advancing theoretical knowledge in a relevant discipline
- Familiarity with TensorFlow, PyTorch or another deep learning framework
- Familiarity with modern software engineering practices (e.g. agile, IDEs, source control, testing, code review)
- Familiarity with statistical reasoning
- Experience working with large datasets
Here at Featurespace we are committed to being a place of equality, inclusion and respect to provide a safe environment for you to bring your authentic self to work. We know that we gain as much strength from our differences as we do our similarities. We value diversity and are dedicated to listening and learning from each other to build and maintain a positive and productive culture. We appreciate this will be an ever-evolving focus for the business to ensure everyone feels supported and has a sense of belonging.