Lead Data Scientist - Deep Learning Practitioner

Capital One Capital One · Banking · London, United Kingdom +1

Lead Data Scientist role focused on developing and deploying deep learning models for underwriting and supporting LLM-based product development in a fintech company.

What you'd actually do

  1. Lead the development of new deep learning approaches to advance our current underwriting models, which form the heart of our lending business. Apply these to new types of (multi-modal) data in order to stay at the forefront of innovation.
  2. Prioritise and own the roadmap for this work. Balancing R&D with in-market results, you will drive ideas from prototypes through to production.
  3. Provide consultancy to our tech and product partners, to help design, develop and launch products powered by Large Language Models (LLMs). This collaboration will help provide seamless experiences for our customers and associates.
  4. Use a combination of business acumen, coding and statistical skills to navigate large amounts of data and extract actionable solutions.
  5. Work cross-functionally on projects that support key business initiatives and drive sustainable growth.

Skills

Required

  • Strong experience developing and deploying deep learning models, particularly for sequential data (e.g. time series, language) using techniques such as LSTMs or transformers.
  • A proven track record leading model development, including setting the technical direction, project management, stakeholder comms, and mentoring junior members of the team.
  • Experience producing and managing reliable and maintainable code in Python in a team setting, including code reviews and setting software engineering best practices
  • Hands-on experience with modern Machine/Deep Learning frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
  • Familiarity with both pre-training and fine-tuning of large-scale models
  • Experience working with structured and unstructured data, such as text, logs, or time series and tokenisation techniques.
  • A strong understanding of probability, statistics, machine learning and familiarity with large data set manipulation.
  • A drive for continued learning through an internal and external focus, and an ability to prototype new techniques to assess value

What the JD emphasized

  • leading the development of proprietary deep learning models
  • advance our current underwriting models
  • support our business partners as they develop advanced servicing products using Large Language Models
  • drive ideas from prototypes through to production
  • launch products powered by Large Language Models (LLMs)

Other signals

  • leading the development of proprietary deep learning models
  • advance our current underwriting models
  • support our business partners as they develop advanced servicing products using Large Language Models
  • drive ideas from prototypes through to production
  • launch products powered by Large Language Models (LLMs)