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• Build & Lead a modern, data-driven credit risk function from the ground up.
• Leverage analytics, predictive modelling, and automation for smarter decisions.
• Shape risk strategies and lead a high-performing team for strategic impact.

Elevate Your Career as a Credit Strategy & Analytics Manager – Retail Banking

We are partnering with a leading financial institution in Papua New Guinea undergoing a significant transformation of its Retail Credit Risk function.

The organisation is building a modern, data-driven credit capability—enhancing decisioning systems, automation, and portfolio analytics to support sustainable lending growth across diverse markets.

This is a rare opportunity for hands-on credit risk analytics professional to shape end-to-end credit decisioning, leveraging data, modelling, and technology to deliver smarter, faster, and more profitable lending outcomes.


About the Role

You will play a key role in building and enhancing credit strategies, decisioning tools, and automated workflows to improve end-to-end lending performance..

Sitting at the intersection of credit strategy, data analytics, and technology, you will design and embed credit strategies directly into decisioning systems—using data to influence how the business originates, manages, and optimises its lending portfolio.

Working closely with Product, Underwriting, Collections, and Data teams, you will develop automated decisioning frameworks and deliver insights into portfolio performance and customer behaviour.

This is a highly hands-on role suited to someone who thrives in data, builds practical solutions, and continuously improves systems and processes..

Key Responsibilities

• Design and embed data-driven credit strategies across retail lending portfolios aligned to risk appetite and growth objectives
• Build and enhance credit decisioning systems, rules engines, and automated workflows to improve speed, accuracy, and consistency in lending decisions
• Develop portfolio analytics, dashboards, and performance frameworks to monitor credit quality, profitability, and emerging risk trends
• Lead the development and refinement of credit models including PD, LGD, EAD, scorecards, and early warning indicators
• Partner with Product, Underwriting, and Collections teams to drive end-to-end lending optimisation and automation
• Translate complex data and modelling outputs into clear, actionable insights to support senior stakeholder and business decision-making

Key Qualifications & Experience

• 7+ years’ experience in credit risk, credit strategy, or portfolio analytics, ideally within retail lending environments
• Strong hands-on experience in credit risk modelling including PD, LGD, EAD, scorecards, and early warning indicators
• Proven ability to work directly with data using SQL, Python, or similar coding and scripting tools
• Experience with credit decisioning systems, rules engines, or workflow automation platforms
• Strong understanding of the end-to-end retail lending lifecycle, including origination, underwriting, portfolio management, and collections
• Demonstrated ability to translate data and modelling outputs into practical credit strategies and business decisions
• Experience in portfolio analytics, performance reporting, and risk-return or profitability analysis
• Strong stakeholder management skills across product, credit, data, and operations in transformation environments

Why Join This Bank?

• Be at the Forefront of Transformation: Lead a modernisation journey where analytics, automation, and innovation reshape credit risk management.
• Strategic Impact: Influence high-level risk decisions that directly impact the bank’s long-term growth and financial stability.
• Collaborative & High-Performing Culture: Work in a dynamic and supportive environment, engaging with leaders across the organisation to shape the future of retail credit risk.
• Exciting Career Growth: This role offers significant exposure to executive leadership and opportunities to develop your expertise in data-driven risk strategy and fintech-driven solutions.

To Apply

To apply, email your CV and other relevant documentation to [email protected]. Please include the Job ID "#26928" in the email's subject to ensure your application is processed.