Lead Analyst – Transactional Banking

Location:West Midlands

Description

RBWM Data and Analytics CoE is Global organization that specializes in providing state of art analytics and solutions to the RBWM Business. The function supports Product, Channel, Pricing, Digital and several other areas within RBWM. The department has more than 300 Machine Learning Users and are building this capability with several use cases. There is the opportunity to leverage advanced analytics to have an impact by delivering, scaling and implementing the solutions to key business problems.

This role reports into the Head of Product and Digital Analytics UK. Lead Analyst is a crucial role, providing the interface between technically focused analysts and outcome driven PnL owners. The lead Analyst will be experienced in stakeholder management and transactional banking, having a good understanding of the retail bank PnL and product composition. At the same time, they will be an experienced analyst or leader of analysts, having good understanding of modelling, statistical tests, forecasting and marketing analytics, being able to design and implement analytical projects from requirement gathering to presenting to senior stakeholders.

Key responsibilities are:
  • Develop or deploy mathematical models and predictive models which aid visualisation of future scenarios or scenarios with altered variables.
  • Develop, test and maintain mathematical models and predictive models for use by internal stakeholders and data analytic teams.
  • Develop programs which automate data collection, assimilation, aggregation and visualisation.
  • Understand business workings and processes so to determine the best manner in which data can be used to provide insights.
  • Produce actionable metrics from automating data collection, assimilation, aggregation and visualisation from multiple data sources including big data.
  • Analyze data from internal and external systems to develop enhanced business value, enhanced predictive capability and increase operating efficiency.
  • Provide consultancy for all stages of the Data Quality Lifecycle, from an execution and assessment perspective, by applying specialist knowledge