Senior Data Scientist - Applied Machine Learning

About the role

Lendi Group, behind Lendi's digital mortgage brand and the Aussie franchise network, is building data and AI capabilities for home lending and property services. As a Senior Data Scientist you will turn customer and business problems into production-ready machine-learning solutions, working across propensity modelling, personalisation, growth and retention. The role covers the full lifecycle, from framing the problem and building features to deployment, monitoring and improvement, and involves close work with data engineers, product managers, analysts, software engineers and business stakeholders.

Key responsibilities

• Translate customer and business problems into analytical and machine-learning objectives.
• Work with large datasets, event data and feature stores to build predictive features.
• Develop, evaluate and improve models for propensity, personalisation, growth and retention.
• Apply classification, regression, experimentation, forecasting and other techniques.
• Take models through deployment, automation, monitoring, retraining and maintenance.
• Improve data quality, feature pipelines, model monitoring, drift detection and ML Ops practices.
• Define success measures with product managers and stakeholders and explain results to technical and non-technical audiences.
• Collaborate with engineering teams on code reviews, documentation and delivery planning, and work with external partners or vendors where needed.

Required profile

• Commercial experience as a Data Scientist, Applied Scientist, Machine Learning Engineer or in a closely related role.
• 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 models in a cloud environment such as Databricks, AWS SageMaker, Azure ML or an equivalent platform.
• Understanding of data pipelines, feature stores, data quality, model monitoring, drift and retraining.
• Ability to work with product managers, engineers, analysts and senior business stakeholders.

Required skills

• Strong Python skills and production-quality code.
• Useful to have: generative AI, large language models or agentic AI; recommendation systems, personalisation or propensity modelling; NLP, time-series forecasting or financial modelling.
• Useful to have: MLflow, Git-based CI/CD, dbt, R or Scala.
• Useful to have: presenting data with Streamlit, Power BI or Tableau.

What we offer

• Hybrid working arrangement.
• Wellbeing programs with 24/7 medical, mental health and financial wellbeing support.
• Paid parental leave of 18-26 weeks for primary carers and up to 4 weeks for secondary carers.
• An extra week of loyalty leave after 3 years' service.