About the position
If you're excited by the idea of using machine learning and data engineering to influence business outcomes in real time, this opportunity is worth exploring.
A growing technology-driven organisation is looking for an experienced Machine Learning Engineering leader who enjoys working at the intersection of data, automation, operations, and decision-making. This is a hands-on role for someone who can combine strong engineering capability with advanced analytical thinking to solve practical business challenges at scale.
Rather than focusing purely on data pipelines or model development, you'll take ownership of systems that help operational teams make faster, smarter, and lower-risk decisions. Your work will directly influence areas such as transaction performance, risk management, compliance oversight, customer experience, and operational efficiency.
You'll work closely with technical and business stakeholders, translating complex data into actionable intelligence while building solutions that are reliable, scalable, and commercially valuable.
Some of the areas you'll be responsible for include:
- Designing real-time monitoring and visibility solutions that provide operational teams with immediate insight into critical business activity and performance trends.
- Building and deploying machine learning models that identify suspicious behaviour, reduce financial risk, and improve the integrity of transaction ecosystems.
- Developing analytical frameworks that detect unusual customer or merchant activity and support regulatory and compliance processes.
- Creating risk assessment models using both traditional and alternative data sources to support decision-making and broaden access to services.
- Enhancing customer due diligence capabilities through automated modelling and continuous improvement initiatives.
- Developing workforce planning, capacity forecasting, growth prediction, and retention models that support operational planning.
- Investigating customer journey friction points by analysing relationships between systems, processes, and user behaviour.
- Identifying recurring failure patterns and operational bottlenecks to improve customer outcomes and reduce support demand.
- Evaluating emerging AI technologies, including large language models and modern analytical tooling, and identifying practical ways to integrate them into business operations.
- Creating intuitive dashboards and visualisations that enable non-technical stakeholders to understand and act on data-driven insights.
This environment will suit someone who enjoys ownership and autonomy. Success in the role requires a balance of technical depth, commercial awareness, and the ability to make progress even when requirements are evolving.
You'll likely thrive here if you:
- Collaborate naturally and build strong working relationships across teams.
- Remain effective when navigating uncertainty and changing priorities.
- Take initiative rather than waiting for direction.
- Stay focused and resilient when solving difficult problems.
- Communicate complex technical concepts in a practical and accessible way.
- Understand that successful machine learning solutions must deliver measurable business value.
- Care about performance, reliability, scalability, and operational impact as much as model accuracy.
- Enjoy improving systems, reducing inefficiencies, and finding smarter ways to work.
- Appreciate cost-effective architecture and enjoy leveraging open-source technologies where appropriate.
- Stay curious about developments across data, analytics, machine learning, and artificial intelligence.
To be considered, you'll need:
- A Bachelor's degree in Computer Science, Engineering, or a related technical discipline.
- At least 10 years of experience working within data science, machine learning, analytics, or closely related fields.
- Advanced proficiency in Python and SQL.
- Experience working with cloud-based data platforms and services across major cloud providers, including data warehousing, storage, analytics, and reporting technologies.
- Exposure to business intelligence and visualisation platforms.
- A versatile mindset and willingness to work across multiple layers of the technology stack, from data infrastructure through to reporting and operational support.
In return, you'll join a highly collaborative team where technical excellence, innovation, and practical problem-solving are genuinely valued. The package includes a competitive salary, generous annual leave, medical benefit support, annual performance incentives, modern equipment, flexible hybrid working arrangements, wellness leave, professional development opportunities, and the chance to contribute to meaningful, high-impact work.
Desired Skills:
- Machine Learning Engineering
- Data Science
- MLOps
- Fraud Analytics
- Risk Modelling
- Cloud Data Platforms
- Python
- SQL
Desired Work Experience:
Desired Qualification Level:
