Manager/Senior Manager - Stress Testing | Group Treasury
About this role
Additional office locations
Sydney
Job ID
23593
Date
19-Aug-2026
Permanent - Full time, Mid-level, Mid-senior
Job category
Group Treasury, Quantitative Analysis
Our team leads enterprise-wide stress testing and provides balance sheet and risk insights across a range of key programmes. We manage the end-to-end stress testing process, oversee model risk activities and deliver climate scenario analysis to support sustainability reporting and climate risk resilience assessments.
We are a global financial services group operating in 30 markets with 57 years of unbroken profitability. At Macquarie, you’re empowered to shape a career that is fulfilling and creates value. You will bring your insights and expertise to the task at hand and feel supported as you make your own kind of impact for a better future.
What role will you play?
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In this role you will lead and strengthen the first-line model risk management framework for stress testing while supporting the delivery and enhancement of climate scenario analysis. You will work closely with Risk, Finance and business teams to oversee model governance, challenge assumptions and methodologies, and translate complex outputs into clear insights for senior stakeholders, governance forums and reporting.
What you offer
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• 4+ years’ of experience in model risk management, model governance, validation, stress testing or a related risk discipline within a regulated financial institution. • Strong understanding of model risk frameworks, governance, controls, policies and standards, including model lifecycle management. • Experience working with model owners, validation teams and senior stakeholders to identify, manage and remediate model risk issues. • Experience in climate scenario analysis, climate risk or financial stress testing, with the ability to assess assumptions, interpret results and understand financial risk impacts. • Strong analytical and stakeholder management skills, with the ability to communicate complex risk insights clearly and drive continuous improvements; experience with Python, data analytics or quantitative modelling is highly regarded.