Data & Risk Decision Analytics Manager

Key Responsibilities

Support Head, Risk Modeling in relation to activities below, be the custody of model repository and accountable for effective model risk management within risk modeling across the region. Technical Expertise

Prepare model performance monitoring reports and accountable for accurate, timely reporting to the management committee. The monitoring report served to provide insights to ensure correct use of model and facilitate critical decision making related to model recalibration or refinement. Review and on-going improvement in model performance monitoring process and automation. Knowledge of scripting particularly in SAS, Python, data savvy and able to work with huge datasets to ensure ongoing model monitoring in accordance to Basel and MFRS requirements. Drill down into data / trend identified from the data. Presents and discusses analysis, model monitoring results i.e. identify area of concern and its thought / rational. Recommend and establish standards, guidelines and processes that improve model monitoring approaches. Comprehensive awareness of the business, regulatory environment, technology and trends and ensure these are reflected in the models monitoring in timely and accurate manner. Stakeholder Management

Strong communication skills. Ability to communicate model monitoring result through compelling narratives within the team as well as to different business leaders and provide value added input to make data driven business decision as well as model enhancement initiatives. Socialize with business expert to gather business insight in the respective area of model monitoring and data analytics. Collaboration within the team and related parties correspondence to data related activities i.e. data derivation rules etc. Requirements

Bachelor Degree - Statistics, Mathematics, Actuarial Science, Economics, Finance, Financial Mathematics. Minimum 3-4 years relevant experiences Good knowledge of relevant regulatory requirements on risk models and risk parameters. Good understanding on the respective area's business products and operations. Good analytical skills. Good statistical modeling knowledge. Good understanding of relational databases and data models. Good programming skills in data handling and statistical modeling.

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