Lead Data Scientist at Lendi Group

Sydney, New South Wales, Australia -

Full Time

Start Date

Immediate

Expiry Date

18 May, 26

Salary

0.0

Posted On

17 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science Team Leadership, Machine Learning Model Design, Statistical Modeling, XGBoost, GBM, LightGBM, Neural Networks, Transformer-based Approaches, Data Product Lifecycle Management, Stakeholder Communication, Python, SQL, Cloud Platforms (GCP/AWS/Azure), Snowflake, Databricks, Docker

Industry

Financial Services

Description

Company Description Lendi Group is a market-leading digital platform business that is focused on revolutionising the entire property journey. It operates Lendi - Australia’s original and #1 digital mortgage brand - and the iconic Aussie franchise. Powered by an advanced AI proprietary platform, a national network of 1,300 brokers, 230 retail stores, a team of experienced buyer’s agents, integrated property search and conveyancing, Aussie is supporting customers at every step of their property journey to find, buy and own with more confidence than ever before. Lendi Group’s loan book is more than $100b and it was named Australia’s Aggregator of the Year for 2024. To learn more about life at Lendi Group check out our blog: https://www.lendi.com.au/inspire/category/life-at-lendi/ Why Join Us? Innovation at Our Core – We challenge the status quo and push boundaries to create better solutions. 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. As a Lead Data Scientist, you'll lead a growing data science team and play a pivotal role in shaping and delivering data-driven solutions across stakeholders. Your primary responsibilities include leading and mentoring your team, designing and implementing advanced machine learning and statistical models, translating complex business problems into scalable data products, and leading the end-to-end lifecycle from discovery to production. You will lead by example with modern data science techniques and tooling, providing clear technical direction and coaching to team members. We’re looking for a collaborative team player who thrives in a dynamic environment, embraces the challenge of legacy systems, remains composed under pressure, and is driven to deliver continuous improvement and strategic impact. This role provides a unique opportunity to connect directly with our stakeholders. You should be a versatile data leader who can both drive hands-on delivery and lead the development of new data initiatives—balancing strategic oversight with practical execution. Job Description On a typical day you will: Lead, mentor and motivate a data science team (up to10 people) and set the technical direction and standards Collaborate within a cross-functional digital team, including Product Management, Engineering and Design. Bridge the gap between business problems and data science solutions, turning opportunities into impactful, production-ready models. Utilise strong analytical and modelling skills to design, build, and validate machine learning solutions, particularly tree-based methods (e.g. XGBoost, GBM, LightGBM), neural networks, and transformer-based approaches. Define and lead the end-to-end lifecycle of data products in an agile environment, from problem framing and experimentation through to deployment and monitoring. Conduct and document detailed analysis, including business case development, to prioritise use cases and clearly communicate trade-offs and expected value to stakeholders. Work closely with data and platform engineers on ETL pipelines, containerised solutions (e.g. Docker, Kubernetes), and cloud-based environments (e.g. GCP, AWS, Azure), with exposure to modern data platforms such as Snowflake and Databricks. Collaborate with cross-functional Agile teams (Engineering, Product, Design, QA) in remote and on-site settings, applying appropriate governance and engagement models to data projects. Provide BAU support for existing models and data products, while actively managing demand, prioritisation, and stakeholder expectations to create space for strategic, high-impact data science initiatives. Qualifications Proven end-to-end Data Science experience (6+ years), including recent experience operating at Senior/Lead level Proven ability to lead, mentor and motivate a data science team (typically 3–10 people) and to set technical direction and standards Strong, hands-on expertise with modern machine learning models and algorithms, particularly tree-based methods (e.g. XGBoost, GBM, LightGBM), statistical models, and neural networks Demonstrated experience working with LLMs, transformers and embeddings in practical, production-oriented use cases Solid understanding of the end-to-end lifecycle of data products, from problem framing and model development through to productisation, pipelining and BAU support Proficiency in Python and a strong command of SQL for data exploration, feature engineering and model implementation Experience working with cloud platforms (e.g. GCP, AWS or Azure) and familiarity with modern data ecosystems such as Snowflake and/or Databricks (or ability to upskill quickly) Exposure to containerised solutions (e.g. Docker) and collaborating with engineers on production deployment and MLOps practices Strong stakeholder management and communication skills, with the ability to translate business problems into data solutions and tell a compelling story with insights Experience contributing to or leading governance and engagement models (e.g. steering committees, RACI/RASCI frameworks, working groups) across multiple stakeholders Demonstrated capability in prioritisation and managing tradeoffs in a resourceconstrained or scaleup environment, including pushing back on lowvalue work Experience developing or contributing to business cases for data and AI initiatives, including articulating value, risks and assumptions 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-GR2 #LI-hybrid Department: Data Design & Emerging Product

Responsibilities

The Lead Data Scientist will be responsible for leading and mentoring a data science team, setting technical direction, and collaborating cross-functionally to translate business problems into impactful, production-ready machine learning models. This includes owning the end-to-end lifecycle of data products from discovery through deployment and monitoring.