Barclays - Assistant Manager - Model Validation

Successfully balance the following responsibilities :

1) The person filling the role is responsible for being model validator for a wide range of models including Fraud, Anti Money Laundering (AML) or Market Surveillance (MS) etc. for different products e.g. Credit Card, Home loan, Unsecured lending etc. A strong focus on Market surveillance models and strategy.

2) Provide independent review (IR) and challenge of different aspects of model (conceptual soundness, model performance etc.) across different model types (Fraud, AML or MS) to a high degree of depth, as required by and detailed in the Bank's policies and standards. This role will be part of Group Risk IVU team.

3) Provide input to/support the governance and reporting processes related to model risk management

Key Accountabilities :

a. Work on independent review of diversified set of models - Market Surveillance, Anti Money Laundering or Fraud models/strategy

b. Perform technical analyses, data analyses, benchmarking, build challenger models (if needed) to support the validation review and challenge process

c. Must be able to challenge others and be open to challenge. Should seek direction on which issues are material but have own views on this also

d. Produce high quality model validation reports, with a particular focus on noting limitations, weaknesses and assumptions

e. Self-study of developments in modelling and validation techniques

f. Should have Anti Money Laundering and Market Surveillance knowledge

Person Specification:

- Experience in a modeller/validator/strategy role in the Market Surveillance, Anti Money Laundering or Fraud areas

- Strong analytical skills with experience in developing, validating and risk management of models

- Should have relevant experience in analytical industry. Hands-on experience in the use of statistical packages, such as SAS, R, Python. Expert user of Microsoft Excel and other Microsoft Office tools

- Knowledge and experience in developing or validating predictive machine learning models (e.g. Random Forests, Gradient Boosted Machines and Deep Neural Networks etc.) on large data sets in Hadoop environments using tools such as Python, Spark and MLLib

- Good communication and influencing skills, ability to produce high quality written communication for technical and non-technical audiences

- Highly organised in terms of documentation and follow through

- An ability to identify and analyse appropriate external data sources for model development or validation