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Day 19
Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
Senior Data Scientist – Applied Machine Learning
Reads like a ML Engineer role: putting models into production.
Sydney, NSW, Australia
Live, first seen by us 19 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingLendi 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.
Turn customer and business problems into analytical and ML objectives
Build predictive features from large datasets, event data and feature stores
Develop and improve models for propensity, personalisation, growth and retention
Take models through deployment, automation, monitoring and retraining
Improve data quality, feature pipelines, drift detection and ML Ops practices
From the employer’s posting
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.
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.
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.
What you’ll bring
All qualificationsCore experience
- Strong Python skills and experience writing maintainable, tested and production-quality code.
- Experience with generative AI, large language models or agentic AI applications.
- Practical experience with classification, regression, feature engineering and experimentation.
- Experience with recommendation systems, personalisation or customer propensity modelling.
- Experience developing, deploying and maintaining machine-learning models in a cloud environment such as Databricks, AWS SageMaker, Azure ML or an equivalent platform.
- Experience with natural language processing, time-series forecasting or financial modelling.
Qualification wording
Strong Python skills and experience writing maintainable, tested and production-quality code.
Experience with generative AI, large language models or agentic AI applications.
Practical experience with classification, regression, feature engineering and experimentation.
Experience with recommendation systems, personalisation or customer propensity modelling.
Experience developing, deploying and maintaining machine-learning models in a cloud environment such as Databricks, AWS SageMaker, Azure ML or an equivalent platform.
Experience with natural language processing, time-series forecasting or financial modelling.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 16, 202619 days ago
- Recorded sightings
- 25
- Last seen by us
- Nov 4, 2026
- Reposted
- once since Oct 7, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
How this job compares
Lendi Group is hiring
1 open job
Open jobs by the week we first saw them, last 12 weeks.
- 100% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Scala
- AWS
- Azure
- Databricks
- dbt
- MLflow
- Power BI
- SageMaker
- Tableau
- Streamlit
Where the posting names them
- 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.
- 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.
- 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 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 with R or Scala. - Experience presenting data and model outputs using tools such as Streamlit, Power BI or Tableau. Additional Information
Your next step
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Already applied? Track this application
Has Lendi Group posted this job before?
Yes. We’ve followed one other Senior Data Scientist – Applied Machine Learning job there since October 2026. It came down after 8 days.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
Ready to contribute to Lendi Group’s next chapter? Apply now and be part of something big!
#lendigroup #LI-AMH1 #LI-hybrid
Company Description
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 environment.
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Sydney, NSW, Australia
- 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.
More source context
Ready to contribute to Lendi Group’s next chapter? Apply now and be part of something big! #lendigroup #LI-AMH1 #LI-hybrid Company Description
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.