Portfolio Evaluation Manager - Tunbridge Wells, Kent
Our client is a successful organisation that specialises in credit card products for the "underserved" segment of the UK population.
The Portfolio Evaluation Manager will assist the team by analysing the data to draw customer behaviour trends within the data. This involves performing customer segmentation using statistical techniques to distinguish the different dynamics within the book by looking at customers at various different yearly cohorts. The analysis should then be fed in a presentation format alongside with the data to support the insight and recommendation. This analysis will be crucial to assist in developing the loss and financial valuation model.
To identify target prospect consumers within the analysis and also to articulate the reasons. It is also important to identify the poorly performing customers and draw insight to enable the comparisons between the populations whilst comparing this customer base to their own base.
Ensuring that the data is loaded and checked through a series of integrity checks to ensure that the data is suitable for analysis, building loss forecasting models and valuation models.
To extract analytical insight from the customers, analyse the performance of the different marketing campaigns/sources/etc and to analyse the transactional data at account management level to assist in developing strategies to treat these accounts to improve the overall profitability of the portfolio. The candidate should have the ability to conduct segmentation, customer profiling, survival analysis to assist in developing insight and also to be able to predict current and future lifetime value of a customer. The candidate will be familiar with a variety of modeling techniques, including logistic regression (binomial and multinomial), decision trees, discriminant analysis, K-means modelling and neural networks.
The successful candidate would have the experience to manage other resources to perform the data loading and validation if and when required.
Whilst data loading and reporting skills are important, this role is mainly focused on delivering data trends, being able to compare different customer base and perform sampling to enable accurate comparisons and prediction in the value of a book over time whilst aligning key activities to different customer segments. The ideal candidate would have the experience to extract information by looking at the data trend and provide a picture of the type of customers and how they have developed over time. (This is not an MI role)
The ideal candidate will be a senior modeller/analyst who is an experienced credit card database marketing expert and also have experience of working in a direct marketing environment preferably focused on the sub prime market.
The ideal candidate would have performed this type of analysis in a previous role.
The candidate must have hands on experience in developing customer segmentation models and be well versed on how to align different strategies (e.g. reprice, revival of dormant accounts,etc) and have the ability to project manage the development work to ensure that it meets the time, cost and quality.
The candidate must also have experience in gauging the timescales for the modelling development work to be carried out and to take full ownership of the delivery of the projects undertaken.
The candidate will be mentoring other team members within the team to develop analysis skills through the use of statistical methods.
The candidate will be at least graduate level and will also have relevant experience in customer response analysis. Data mining, statistical analysis and spread sheet based decision support experience essential.
A good knowledge of SAS Base, SAS Stat and writing queries to interrogate data is essential.
Knowledge of MS Excel is desirable, especially in creating pivot tables to enable the other team members to look at the data trends.
Good interpersonal and influencing and skills. Must be able to present articulate and well thought-through business cases for the implementation of models to senior management.
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