Overall purpose of role
* Analyse credit risk across the portfolio, to understand trends in risk and customer behaviour and the reasons behind them. This may involve building new sorts of tools and analytical techniques.
* Use this insight to find ways to optimise our ECM programmes and customer treatment, to improve our portfolio's risk profile and returns.
* This role is part of the Credit Strategy Analytics team that shapes UK cards strategy. With a portfolio in excess of £16bn and more than amillion new applicants each year, information and analytics is critical to our decision making, and this role will require strong conceptual thinking and analytical ability.
* You will investigate our exposure to different kinds of credit risk, and our resilience in times of market stress, and propose frameworks and/or activities to manage these.
* You will help develop analytical ways to identify, understand, and manage credit risk on our portfolio.
* You will have a role in formulating policy and strategy for the UK card portfolio - both in setting and reviewing risk "guardrails" and "decision frameworks" and directly proposing changes to strategy where supported by analysis.
* Underlying all of this is our ability to understand and engage with our customers' needs.
Stakeholder Management and Leadership
* Optimisation isn't just about credit risk, so you'll work with change management, commercial, financial and operational teams to make decisions that are best for both our customers and our company.
* Our unparalelled experience and data mean that you'll have great scope to drive major improvements in the business through careful analysis and strategy optimisation. You'll keep business leaders in the know as you monitor and react to new information, and present your strategies simply and effectively to senior stakeholders and other members of your business team, and see them all the way through to implementation and beyond.
Decision-making and Problem Solving
* You will work with large datasets, looking to understand what information is effective at predicting different customer behaviours.
* Mastering decision trees, regressions, scorecards, and other modelling techniques will be critical to distill this into insight and action.
* You will build and use your credit expertise to come up with effective hypotheses to test, and to reduce complex problems to solvable questions.
* You must have a degree in a numerical discipline, with experience of modelling and solving numerical and statistical problems - for example, physics, mathematics, or engineering.
* You will have some experience of large-scale analytics (eg. understanding behaviours that manifest in large numbers of people or large experiments - you will not be underwriting customers one-by-one!)
* You will have some experience of risk analysis in a consumer finance context (eg. mass market loans, credit cards, etc.) and have some experience of common consumer credit behaviours.
Essential Skills/Basic Qualifications:
* You will have experience working with databases (eg. via SQL or SAS) and using SAS for data analysis and mining tools on large datasets.
* You will be effective at using Excel to quickly and accurately analyse results (eg. familiar with most of the following: pivot tables, conditional formatting, inbuilt function, array formulae)
* You will have a solid grasp of statistical techniques (eg. familiar with most of the following: measurements of mean and variance, design of experiments, multivariate regression, logistic regression, confidence intervals, hypothesis testing)
* You will be able to identify key numerical concepts relating to consumer lending, and formulate them into simultaneous equations and then solve them.
Desirable skills/Preferred Qualifications:
* You will be effective at communicating within your peer-group and upwards (eg. summarising the results of your analysis sucinctly to senior stakeholders and peers).
* Advanced mathematical and statistical skill such as familiarity with neural network models, "deep learning", stochastic models, tree networks, etc. - but you not have applied these "in the real world"
* You will be enthusiatic and effective when it comes to turning your insight into action.
* You will have a functional understanding of how a consumer lending product functions and how the key parts of its P&L might interact.