Lendi Group is transforming the way Australians find, buy and own property. As the company behind Lendi — Australiaʼs original and #1 digital mortgage brand — and the iconic Aussie franchise network, we combine technology, distribution and deep property expertise to support customers across their entire property journey. Powered by our proprietary AI platform and a national network of 1,300 brokers and 220 retail stores, Lendi Group now manages a $107B+ loan book and continues to set the pace in the industry, recently recognised as Aggregator of the Year.

Through our integrated Find–Buy–Own ecosystem and industry-leading Agentic platform, weʼre redefining how property decisions are made — delivering greater confidence, speed and simplicity for customers.

This role sits at the centre of that next phase of growth.

To learn more about life at Lendi Group check out our blog: https://www.lendi.com.au/inspire/category/life-at-lendi/

- Innovation at Our Core– We challenge the status quo and push boundaries to create better solutions, with a goal of becoming AI-native by June 2026.
- Work with the Best– Collaborate with some of the brightest minds in fintech, financial services, and strategy.
- Make an Impact– Contribute to meaningful projects that shape our business and the future of property finance.
- Grow & Evolve– Develop your skills and advance your career in a fast-moving, purpose-driven setting.

About the role

Lendi Group is building data and AI capabilities that help customers make better decisions across home lending and related property services.

As a Senior Data Scientist, you will turn complex customer and business problems into practical, production-ready machine-learning solutions.



You will work across propensity modelling, personalisation, growth, retention and other applied data-science use cases, using techniques such as classification, regression, experimentation and predictive analytics.

This is a hands‑on role covering the full lifecycle: framing the problem, understanding the data, developing features and models, deploying solutions, monitoring performance and improving them over time. You will work closely with data engineers, product managers, analysts, software engineers and business stakeholders to deliver measurable outcomes.

Generative and agentic AI will form part of the team’s evolving toolkit, where it is the right solution to the problem. The core of the role is strong applied machine learning and sound data‑science judgement.

- Translate customer and business problems into clear analytical and machine-learning objectives.
- Work with large datasets, event data and feature stores to identify useful signals and build predictive features.
- Develop, evaluate and improve models for use cases such as propensity, personalisation, growth and retention.
- Apply appropriate techniques across classification, regression, experimentation, forecasting and other applied data‑science problems.
- Take models through the full product lifecycle, including deployment, automation, monitoring,



retraining and ongoing maintenance.
- Help improve data quality, feature pipelines, model monitoring, drift detection and ML Ops practices.
- Work with product managers and business stakeholders to define success measures and translate model outputs into action.
- Explain technical approaches, assumptions and results clearly to technical and non-technical audiences.
- Collaborate with data, software and AI engineering teams to review solutions, improve coding standards and share knowledge.
- Contribute to technical documentation, design discussions and delivery planning.
- Work with external partners or vendors where required to deliver the right solution.

- Commercial experience as a Data Scientist, Applied Scientist, Machine Learning Engineer or in a closely related role.
- Strong Python skills and experience writing maintainable, tested and production-quality code.
- Strong foundations in probability, statistics, model evaluation and machine-learning fundamentals.
- Practical experience with classification, regression, feature engineering and experimentation.
- Experience developing, deploying and maintaining machine-learning models in a cloud environment such as Databricks, AWS SageMaker, Azure ML or an equivalent platform.
- An understanding of data pipelines, feature stores, data quality, model monitoring, drift and retraining.
- Experience working across the full model lifecycle, from problem definition through to production and ongoing improvement.
- The ability to work effectively with product managers, engineers, analysts and senior business stakeholders.
- A collaborative approach to code reviews, documentatio

📌 Senior Data Scientist – Applied Machine Learning (Australia)
🏢 Lendi Group
📍 Australia