Roles and Responsibility
Strategy

Review of FCC models
Interaction with external stakeholders , Internal and external auditors, regulatory bodies

Business

Processes

Risk Management

Perform an independent validation of new and existing models that are used in Financial crime risk management, anti-money laundering, transaction monitoring, screening, fraud risk management etc.
Qualitative review of model development process including underlying assumptions & theoretical basis.
Quantitative assessment of model performance via data evaluation and statistical testing.
Documentation of validation findings and communication of results to senior management and presentation to relevant committees.
Coordination with internal stakeholders on model issues, achieving suitable resolutions.
Manage and complete the model validation from end to end, meeting the planned timelines and required standards.
Recommend improvements in the models.
Review regulatory requirements and industry practice regarding the models.
Assist Head of Model Validation in addressing concerns or questions relating to the models.

Governance

Regulatory & Business Conduct

Display exemplary conduct and live by the Group’s Values and Code of Conduct.
Take personal responsibility for embedding the highest standards of ethics, including regulatory and business conduct, across Standard Chartered Bank. This includes understanding and ensuring compliance with, in letter and spirit, all applicable laws, regulations, guidelines and the Group Code of Conduct.
Lead the Global Model Validation group to achieve the outcomes set out in the Bank’s Conduct Principles.
Effectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters.

Key Stakeholders

Our Ideal Candidate

At least graduate level qualifications in statistics, banking, finance, econometrics, mathematics or related quant field.
7-10 years of experience in quantitative modelling and/or model validation, with focus on the Credit risk or AML / Compliance or Fraud Risk Management related model development or validation.
Proficient in statistical and large datasets analysis using software solutions including SAS, R, Python.
Thorough understanding of Artificial Intelligence and Machine Learning models and algorithms.
Excellent understanding of models, processes and/or industrial guidelines related to credit risk management, client risk assessment, transaction monitoring, screening, and fraud risk management.
Strong focus on quality control and attention to detail.
Curious, with ability and experience of speaking up and challenging perceived wisdom.

Role Specific Technical Competencies

Expertise in analytics, developing or validating statistical models within banking industry. Experience in digital/online lending models/applications preferred.
Good understanding and experience in credit risk modelling, and/or stress testing analysis. Expertise in machine learning techniques and algorithms (SVM, k-NN, Tree, Boosting(i.e. XgBoost, Ada, Light etc.), Bagging(i.e. Random Forest), Ensemble/Stacked models etc.)
Proficient in statistical and data analysis using data management and statistical software which includes SAS, R, Python, Excel etc.
Strong communication and project management skills.
Strong focus on quality control and attention to detail.
Knowledge of banking data and IT infrastructure, including data management and data quality control
Effective presentation and business engagement skills at senior executive level.

Associate Director - Risk Review