MGR/AVP, Data Scientist, Credit Risk, Risk Portfolio Management (RPM)

About OCBC Bank

OCBC Bank is the longest established Singapore bank, formed in 1932 from the merger of three local banks. It is now the second largest financial services group in Southeast Asia by assets and one of the world’s most highly-rated banks, with an Aa1 rating from Moody’s. Recognised for its financial strength and stability, OCBC Bank is consistently ranked among the World’s Top 50 Safest Banks by Global Finance and has been named Best Managed Bank in Singapore by The Asian Banker.

Division Description: Group Risk Management (GRM)

GRM is an independent function responsible for ensuring that risk management practices at OCBC Bank are effective and comprehensive. GRM builds and drives the Bank's businesses through an integrated risk management approach relying on strong risk analytics to support strategic business decision-making and to create a competitive edge for the Group.

Department Description: Risk Portfolio Management (RPM)

RPM comprises a range of functions primarily focused on credit portfolio management within OCBC Group. These include:

• Assess risk / opportunities in the context of risk appetite & macro conditions.
• Analyse portfolio performance. Identify trends & drivers, draw insights & develop recommendations.
• Manage risk measurement framework such as scorecards & rating models, RWA approach, risk data & systems infrastructure, policy & processes. These are used in underwriting, limits, early warning, capital & provision level assessments.
• Manage portfolio dashboards / reports to stakeholders.

Key Responsibilities

• Employ advanced analytics and data visualisation to support credit portfolio management and business needs.
• Build predictive models using machine learning to facilitate credit decisioning and early warning.
• Deploy, monitor and maintain the models on production credit systems.
• Work with data engineers to construct data pipelines to integrate new data feeds.
• Advance the discussion on governance and assurance regarding the use of artificial intelligence and machine learning in credit risk applications.

Job Qualifications

The Ideal Candidate Would Meet The Following Requirements:

• Bachelor’s or postgraduate degree in Data Science, Computer Science, Mathematics or an equivalent quantitative area.
• At least 3 years of relevant experience.
• Proficiency in data science, machine learning, building models and deploying/maintaining them in production.
• Proficiency in machine learning frameworks, Python, Spark, Hadoop, Linux/Unix environments, Git, CI/CD pipelines and MLOps.
• An enthusiastic team player who can figure it out, get stuff done, have fun and is excellent in communication and stakeholder management.
• Experience in banking, retail credit, or credit risk analytics and modelling would be an advantage.
• Candidates with more experience would be considered for a more senior position within the team.