Job description

Lead Data Scientist

FinTech

Permanent

£100,000 - £115,000 + benefits

London (hybrid)

Xcede are excited to be partnering with a major FinTech organisation who are looking for a Lead Data Scientist.

You will work across all business areas and will manage several projects simultaneously, allowing the opportunity to work across a range of tools and technologies. This is an end to end role, so you will work across all elements of the Data Science lifecycle: R&D, scoping, ML model building, feature selection, deployment and monitoring. There is also a lot of stakeholder management and business sense required for this position. It is worth noting that the team are embarking on a range of Generative AI projects, so lots of interesting cutting edge technology to be working on. You will be required to managed a team of 3-4 Data Scientist's whilst remaining hands on.

Key skills/experience required:

  • A degree in STEM subject, ideally a Master's or PhD.
  • 5+ years industry experience
  • 2+ years' experience managing a small Data Science team
  • Understanding of machine learning techniques including linear models and tree-based methods / clustering / unsupervised ML techniques
  • ML model deployment and scalability experience including ML Ops
  • Experience developing ML solutions to Cloud (AWS/GCP/Azure)
  • Excellent grasp of Python/SQL + machine learning core libraries
  • Ability to convey technical concepts to a non-technical audience
  • Great scientific thinking, the ability to design effective experiments which help us learn about new problems in the most efficient way
  • Good knowledge of software development best practices
  • An interest in Generative AI
  • Experience in the FinTech space is nice to have

For more information, please send an up-to-date CV to nathan.carolan@xcede.com

Lead Data Scientist, Data Science, Machine Learning, Python, Computer Science, Statistics, Data pipelines, SQL, Regression, Clustering, XGBoost, Random Forest, Decision Trees, Predictive modelling, FinTech, ML Ops, NLP, Deep Learning, Neural Networks, Deployment, Containerisation, Generative AI, LLMs, large language models

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