Job description

About Decision Science

Sitting within our Risk function, Decision Science is a centre of excellence for analytics, modelling and customer insights. The team support the wider Risk division by:

  • Delivering sophisticated data analytics and modelling to support retail and commercial customer needs, business needs and credit decisions across the credit lifecycle
  • Supporting the bank's prudential and regulatory requirements such as capital and impairment allocation, and capital optimisation

We work with data that underpins vital business decisions, and together we make it possible to add customer value, control risk, and help to build a safe, strong bank for our customers.

Our roles are available in a variety of locations, and we support flexible and family-friendly ways of working.

The role of a Machine Learning Engineer in Decision Science

Working in multidisciplinary teams you will support all stages of a project - exploring the problem statement and experimenting with different approaches to optimise the delivery and deployment of analytical solutions.


As a Machine Learning Engineer, you will perform the following activities:

  • Develop solutions in Python/SAS to optimise the efficiency of the wider Decision Science team's delivery.
  • Work with the wider team to understand their day-to-day challenges, identify opportunities for improvement and support the upskilling of using new tools, technology and ways of working.
  • Collaborate in an agile fashion with multidisciplinary teams to optimise/deploy analytical solutions.
  • Support the transition to the future technology such as using the Cloud.
  • Deepen your understanding of code optimisation within predictive analytics and/or machine learning.

The Person

We welcome candidates from all personal and career backgrounds and support flexible working arrangements. We will help you reach your full potential, meet your career aspirations and really make a difference as you embark on a career with excellent development and progression opportunities.

Requirements:

  • Minimum 2 years' industry experience in Python (e.g. Pandas, NumPy, Scikit-learn, etc…)
  • Use of Code Repositories (e.g. GitHub / Git)
  • Experience in optimising and automating/deploying analytical processes
  • Delivering solutions as an individual or as part of a wider team

Desirable:

  • Python web application (Flask, Django, Dash) & documentation (MKDocs, Sphinx) experience
  • Familiarity with Orchestration frameworks (Airflow, Kubeflow Pipelines)
  • SAS experience
  • Cloud Computing experience
  • DevOps (Jenkins, Sonar) experience
  • Experience in training and supporting team members including non-technical colleagues

In return:

We will recognise and reward your performance. Our award-winning benefits package includes:

  • A competitive salary and annual performance-related bonus
  • A generous annual holiday allowance with the option to purchase up to five additional days per year
  • An additional annual flexible allowance that you can use to choose from a wide range of benefits, such as Cycle2work and enhanced medical benefits, or take as cash
  • A contributory pension scheme
  • Private medical insurance

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