Operational Risk Ai Modelling Lead

**Req ID**: 91941
**Department**: Risk Australia R&C - Retail Risk
**Division**: Risk
**Location**: Melbourne
About Us
At ANZ, we're shaping a world where people and communities thrive, driven by a common goal: to improve the financial wellbeing and sustainability of our millions of customers.
About the Role
The Operational Risk AI Modelling Lead is a critical role within our organization, designed to spearhead the development and implementation of advanced AI models including LLMs, to identify, assess, and mitigate operational risks. Reporting into the Head of Australia Risk Modelling, this role exists to ensure that our risk management strategies are data-driven, leveraging cutting-edge AI technologies to enhance the accuracy and efficiency of risk detection and response. The Operational Risk AI Modelling Lead will collaborate with cross-functional teams to integrate AI solutions into our risk management framework, ultimately safeguarding the organization against potential operational threats and ensuring regulatory compliance.
Banking is changing and we’re changing with it, giving our people great opportunities to try new things, learn and grow. Whatever your role at ANZ, you’ll be building your future, while helping to build ours.
**Role Type**: Permanent
**Role Location**: Melbourne
**Work Hours**: Full Time
What will your day look like?
**You will be accountable for**:

- Develop and implement AI models that enhance the accuracy of risk assessments. challenge inefficient practices, influence change, and implement solutions to support the Enterprise Analytics roadmap
- Ensure that AI-driven risk management solutions are designed to protect customer interests, maintaining high levels of trust and satisfaction, and improve customer financial wellbeing.
- Lead the design, development, and deployment of AI models for operational risk management and second-line-of-defence review.
- Work closely with cross-functional teams to ensure seamless integration of AI models into existing risk management frameworks.
- Mentor and guide a newly established data scientist team, fostering a culture of innovation and continuous learning. Lead the design of Generative AI solutions for delivering best practice solutions.
- Engage with key stakeholders (both internally and externally, locally and globally) to communicate the benefits and progress of AI initiatives in risk management.
- Ensure that all AI models and risk management processes comply with relevant regulatory requirements and industry standards.
- Identify potential risks associated with AI model deployment and develop strategies to mitigate these risks.
- Work with technical experts, data scientists and engineers to build and maintain scalable pipelines for machine learning and LLM models. Research, develop and implement leading edge technologies designed to review customer process outcomes and detect operational risk and compliance events using advanced AI, NLP and LLM techniques. Ensure ANZ data quality, governance, and security standards and frameworks are followed. Integrate diverse data streams using APIs and external sources.

What will you bring?
To grow and be successful in this role, you will ideally bring the following:

- Extensive experience in AI/ Machine learning modelling within the financial services industry, with a proven track record of solving complex problems using advanced analytics, developing and implementing AI models.
- Technical Skills:

- Experienced data scientist or statistician with ML skills.
- Previous experience with big data technologies (e.g., Spark, Hadoop, BigQuery, Snowflake) and cloud platforms (AWS, GCP, Azure).
- Familiarity with data lake and warehouse solutions for large-scale data processing.
- Strong problem-solving skills and ability to lead fast-paced, data scientist team that is working with other cross-functional teams
- Demonstrated proven ability in designing models that cater to solving a business problem using appropriate sample design methodology, modelling data preparation skills (cleaning, exploration, imputation), technique selection and modelling workflow.
- Experienced in working with data engineers to design and build high-quality, scalable data products and robust, fault-tolerant data pipelines to clean, transform, and aggregate data.
- Strong analytical skills to translate data insights and modelling outcomes into actionable business recommendations. Developed mechanisms to track performance and value realization.
- Experience in identifying, monitoring, and mitigating risks, issues, and dependencies.
- Deep understanding of customer needs and behaviours, with a commitment to optimizing customer journeys and experiences.
- Extensive experience working in cross-functional, agile, or continuous delivery teams. Skilled at gathering progress updates and effectively communicating with key stakeholders.
- Commitment to continuous improvement, engaging with others to gain buy-in, and acting as an advocate