AVP, Credit Risk Data Scientist, Credit Risk Modelling

OCBC; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

OCBC is seeking a Credit Risk Data Scientist to lead development and deployment of advanced analytics and ML models that assess credit risk across Consumer, SME and Wholesale segments. You will handle large datasets, build predictive models, and translate outputs into actionable risk insights for decision making.

The role emphasizes collaboration with stakeholders, adherence to model governance, and interaction with auditors and regulators, in a fast-paced banking environment with strong

Qualifications

• Degree in a quantitative discipline such as Data Science, Statistics, Mathematics or Computer Science.
• 5-7 years of relevant experience in credit analysis/modelling or credit risk management.
• Experience with big data tech (Hadoop, Hive, Trino, Spark) and DevOps tools (Jira, Jenkins, Git).
• Proficiency in machine learning tools/frameworks (Scikit-Learn, TensorFlow, PyTorch).
• Analytical and independent thinker with strong written and verbal communication skills.
• Ability to interact and communicate effectively with senior management.
• Willing to take on new challenges in a fast-paced environment.

Responsibilities

• Develop, implement, and maintain machine learning credit risk models for Consumer, Small Business and Wholesale segments.
• Monitor, back-test and report model performance to ensure governance and early weakness detection.
• Define and maintain system parameters and configurations housing the models.
• Develop deep expertise in credit risk modelling methodologies.
• Collaborate with validators to ensure compliance with governance and timely closure of findings.
• Engage with auditors and regulators to ensure regulatory compliance.
• Work with stakeholders to apply model outputs in credit decisions, strategy, allowances and capital assessment.

Skills

Credit risk modelling

Statistical analysis

Big data

Data science

ML frameworks

Stakeholder comms

Education

Degree in quantitative discipline

Tools

Hadoop

Hive

Trino

Spark

Jira

Jenkins

Git

Job description

Why Join

As a Credit Risk Data Scientist, you will be part of a team that drives the development of advanced analytics and machine learning models to assess and manage credit risk. You will have the opportunity to work with large datasets, develop predictive models, and influence business decisions. Join us and contribute to the bank's risk management capabilities, while building a rewarding career in a dynamic and supportive team.

How You Succeed

To succeed in this role, you will need to develop and implement advanced analytics and machine learning models to assess credit risk. This involves collating and analyzing large datasets, identifying patterns and trends, and developing predictive models that can inform business decisions. You will also need to work closely with stakeholders to understand their needs and develop solutions that meet their requirements.

What You Do

• Develop, implement, and maintain machine learning credit risk models supporting the Consumer, Small Business and Wholesale segments of the Group.
• Monitor, back‑test and report performance of the models to ensure adherence to performance standards and early detection of weaknesses.
• Develop and maintain user requirements, parameters and configurations of systems housing the models.
• Develop in‑depth expertise in credit risk modelling methodologies.
• Work closely with independent model validators to ensure compliance to model governance framework and timely closure of validation findings.
• Engage with auditors and regulators to ensure compliance with relevant requirements.
• Engage with various stakeholders to develop analytical solutions using model outputs in credit decisioning, business strategies, allowance, and capital assessment.

Who You Work With

Group Risk Management works independently to protect, build, and drive our businesses. The team supports good decision-making with strong risk analysis and a crucial, comprehensive role in sharpening our competitive edge. Optimising risk‑adjusted returns, it is about seeking and adopting best‑in‑class practices, protecting the group from unforeseen losses, keeping risk within appetite, and embracing change and managing growth in one of the world's strongest banks.

About The Team

CRM is a high‑profile, multi‑disciplinary risk analytics team that covers credit risk models at OCBC Group. The key functions CRM performs include developing, implementing and managing various types of credit risk models, such as Credit risk Scorecards, Internal Rating models, IFRS 9 based Expected Credit Loss models, Credit Stress Testing models, Economic Capital models and Machine Learning models that support Group’s credit risk measurement. These models are embedded in the credit underwriting, customer selection, limit setting, early warning and problem recognition, as well as assessment of capital and provision adequacy.

Who You Are

• Degree in a Quantitative discipline, such as Data Science, Statistics, Mathematics or Computer Science.
• Has 5-7 years of relevant experience in credit analysis/modelling or credit risk management of Consumer, Small Business and/or Wholesale portfolios.
• Experience with big data technologies such as Hadoop, Hive, Trino and Spark as well as DevOps tools such as Jira, Jenkins and Git.
• Proficiency in common machine learning tools and frameworks (Scikit‑Learn / Tensorflow / PyTorch).
• Analytical and independent thinker with strong written and verbal communication skills.
• Ability to interact and communicate effectively with senior management.
• Willing to take on new challenges and work in a fast‑paced environment.

What We Offer

Competitive base salary. A suite of holistic, flexible benefits to suit every lifestyle. Community initiatives. Industry‑leading learning and professional development opportunities. Your wellbeing, growth and aspirations are every bit as cared for as the needs of our customers.