Senior Data Scientist – Applied Machine Learning

Lendi; Sydney; Hybrid; AUD 130,000 - 180,000; Full time

Benefits offered by this job

Hybrid working

Wellbeing programs

Parental Leave 18-26 weeks

Loyalty Leave

Diversity and inclusion

Job summary

Lendi Group is seeking an experienced Data Scientist in Sydney, Australia to translate business problems into machine-learning objectives and build predictive features from large datasets. You’ll develop, evaluate and deploy models for propensity, personalisation, growth and retention, applying classification, regression, and time-series forecasting while maintaining ML pipelines.

You’ll work with product managers, engineers and analysts to define success and translate outputs into actions.

Qualifications

• 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, documentation, knowledge sharing and continuous improvement.
• Good judgement about when to use a simple, explainable approach and when a more advanced technique is justified.

Responsibilities

• 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.

Skills

Python

Statistics

Model evaluation

ML fundamentals

Classification

Cloud platforms

Data pipelines

Model monitoring

Team collaboration

Code reviews

Tools

Databricks

AWS SageMaker

Azure ML

MLflow

dbt

Git CI/CD

R

Scala

Streamlit

Power BI

Tableau

Job description

Job Description

• 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.

Qualifications

• 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, documentation, knowledge sharing and continuous improvement.
• Good judgement about when to use a simple, explainable approach and when a more advanced technique is justified.

It would be useful if you also have

• Experience with generative AI, large language models or agentic AI applications.
• Experience with recommendation systems, personalisation or customer propensity modelling.
• Experience with natural language processing, time-series forecasting or financial modelling.
• Experience with MLflow, Git-based CI/CD, dbt or related data and ML engineering practices.
• Experience with R or Scala.
• Experience presenting data and model outputs using tools such as Streamlit, Power BI or Tableau.

Additional Information

Lendi Group teams operate across Australia and the Philippines. Bringing together diverse expertise, innovative technology and a customer-first approach, our teams work seamlessly to simplify the property journey, helping Australians find, buy and own property with confidence.

We support our people in a variety of ways, but a few of the benefits that our people rave about include:

• A vibrant, relaxed, yet professional culture.
• Hybrid working arrangement designed to support work-life balance, while fostering meaningful connection and collaboration.
• A holistic wellbeing programs offering 24/7 support, including medical, mental health, and financial wellbeing services to enable our workforce to thrive at home and work.
• Generous paid Parental Leave: we celebrate our growing Lendi Group family with 18-26 weeks leave for primary carers and up to 4 weeks for secondary carers.
• An additional week’s Loyalty Leave each year after reaching 3 years’ service.
• Wellness initiatives with a strong focus on psychological safety.

We’re committed to fostering a diverse and inclusive community at Lendi Group. We believe that a team reflecting the world around us leads to greater innovation, stronger collaboration, and a more engaging workplace.

Our culture is guided by our 3 core values: We Are Stronger United; Act Like You Own It; and Keep Home Loans Human. Our values are part of our core DNA that helps Lendi Group to attract, engage and evolve the right talent and build best-in-class products.

This is an opportunity to shape the future of a fast-growing, purpose-driven company that’s transforming the homeownership journey.