Data Scientist (Autolend)

  • London
  • Full time
  • hybrid
  • Autolend Team

About the role

You will be working on Autolend, our vehicle financing product. You will own the machine-learning models used for automated risk-assessment, as well as picking up other projects related to automation of document processing, and pricing.

Join us if you want to

  • Build best-in-class machine learning systems that are core to the success of our business.
  • Own the deployment and monitoring of your models in production. 
  • Work in small teams where you are trusted to take ownership and make decisions quickly.
  • Be resourceful to solve problems and find smarter solutions than the status quo.
  • Our team's objectives

  • The data science team develops proprietary risk models which are core to the company’s success. 
  • We work across the business in a multidisciplinary capacity to identify issues, translate business problems into data questions, analyse and propose solutions.
  • We self-serve with all deployment and monitoring, without a separate machine-learning-engineering team. 
  • How you'll impact those objectives

  • Learn the domain of products that Lendable serves, understanding the data that informs strategy and risk modelling is essential to being able to successfully contribute value.
  • Rigorously search for the best models that enhance underwriting quality.
  • Clearly communicate results to stakeholders through verbal and written communication.
  • Share ideas with the wider team, learn from and contribute to the body of knowledge.
  • Key skills

  • Experience using Python and SQL
  • Strong proficiency with PyData stack 
  • Knowledge of machine learning techniques
  • Confident communicator and contributes effectively within a team environment
  • Self driven and willing to lead on projects / new initiatives
  • Nice to have

  • Interest in machine learning engineering
  • Interest in data engineering
  • Prior experience with credit risk modelling
  • Prior experience with use of LLMs for document question answering
  • Interview process

  • A phone call with one of the team
  • An exercise to complete in your own time  
  • Onsite Interviews; 
  • Discuss the exercise you completed
  • Meet the team you’ll work with daily 
  • Meet the exec team