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Senior Analyst Credit Risk Modeler


Job Description

This is a senior modeller role reporting into the Senior Manager of Retail Credit Risk. Your role will span across Mortgages, Credit Cards, and Personal Loans & SME retail portfolios and across the credit life cycle. 

You will be required to support the development, maintenance and deployment of IFRS 9 provisioning and portfolio monitoring models (Early warning system) ensuring that they comply with the relevant regulations and are fit-for-purpose. You will also be involved in IRB Model development and management with a focus on LGD models. The role will have oversight over the development and management of collection strategies.

What are you going to do?

  • You will be developing and contributing towards leading practice statistical and analytics credit models to calculate credit risk ratings, loss estimates, risk-weighted assets, stress testing, provision models (IFRS9) and regulatory capital requirements across retail products.

  • Involved in IRB Model development and management with a focus on LGD.

  • Develop Credit models to calculate Credit Risk ratings, loss estimates, risk weighted assets, stress testing, provision models (IFRS9) and regulatory capital requirements across retail banking products.

  • Maintain and enhance existing credit risk models, and build new models to estimate credit risk relating to new business partners

  • Prepare modelling to assist in forecasting, provisioning and residual value risk

  • Producing board reports, risk reports preferably ECB and APRA reporting

What are we looking for? Someone who has…

  • Atleast 3+ years of deep understanding in developing risk models (IFRS9, EWS, IRB Models) in Banking - Sector knowledge and experience of working in the area of Credit Risk; ideally experience of working with Credit Risk models.

  • A degree in a numerate subject (e.g. mathematics, statistics, operational research, economics or MBA) from a reputed institute with knowledge of advanced statistical and analytical techniques.

  • Extensive experience manipulating and analysing large amounts of complex data, building and implementing predictive models.

  • Strong programming skills using Advanced SAS, Python, and SQL.

  • Hands on experience in quantitative analysis, statistical modeling, ML modelling, loss forecasting, loan loss reserve modeling, and econometric modeling for retail portfolios.

  • Prior experience of Credit risk/rating model development/validation procedures and concepts and ability to manage own assignment independently.

  • Good team management and communication (both written and verbal) skills, including the ability to lead and motivate others.

  • Engages with a diverse group of stakeholders to influence direction of projects to meet the project milestones.

  • Good to have worked in agile environment.

  • “Can do” attitude and be resilient

If you meet a number of the requirements, but not all, we encourage you to submit your application.

About Us

At ING, we want to make life simpler and more worthwhile – for everyone who banks with us, for the people who work with us, and the community at large, too.

When you come to work at ING, you’re joining a team where individuality isn’t just accepted, it’s encouraged. We’ve built a culture that’s fun, friendly and supportive – it’s the kind of place where you can be yourself and make the most of whatever you have to offer.

We give people the freedom to take risks, think differently, take ownership of their work, and make great things happen. We’re here to help you get ahead. And with our global network, there’s plenty of scope to take your career in new directions, perhaps even ones you’ve never considered.

People of all ages, sexual orientations, cultures and backgrounds are welcome to apply – likewise if you’re an indigenous Australian, or you’re living with a disability, or you have family or caring responsibilities.

Sound like the kind of place you’d feel at home? We’d love to hear from you.

(One last thing, ING operates a direct talent sourcing model. So no agency introductions, please.)