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Sydney, New South Wales, Australia
Hybrid
We're REA
With bold and ambitious goals, REA Group is changing the way the world experiences property. No matter where you're at on your property journey, we're here to help with every step - whether that's finding or financing your next home.
Our people are the key to our success. At the heart of everything we do, is a thriving culture centred around high performance and care. We are purpose driven and collaborative, which drives innovation and our ability to make a real impact. As such, we’re proud to have been named in Australia’s “Top 5” Best Workplaces two years in a row, as well as being recognised as a Best Workplace for Women.
Which team is this in
PropTrack is Australia’s leading source of property insights, digital valuation solutions, and predictive analytics. The PropTrack Data Science Team will focus on experimenting with new statistical and machine learning techniques, optimising existing models, and prototyping new models. The role will involve working collaboratively with ML Engineers to productionise all model improvements as we aim to empower every property journey with trusted insights, AI, and seamless experiences.
REA is evolving how it leverages AI and data to deliver benefits for customers. Following significant growth in recent years, we are now looking for an experienced Data Scientist to help enhance our existing residential property Automated Valuation Model (AVM), extend our suite of market metrics, and build innovative, customer focused products.
Day to day of the job
Contribute to design and development of AI solutions using AWS, Docker, Python, BigQuery, and other technologies.
Apply domain knowledge to extract, manipulate and process data from multiple sources and assemble datasets for modelling
Finding or engineering new features that can be ingested into PropTrack’s AI platform
Prototyping and building new statistical and machine learning models to improve model performance
Hyperparameter tuning and cross-validating model performance
Develop and maintain model visualisation tools for analytics and business support
Document projects and processes on Confluence and Git repositories
Project presentations within the data science team and to broader REA audience
Who we’re looking for
Tertiary qualifications in data science, econometrics, statistics, mathematics, or related discipline with a numerical/quantitative focus OR equivalent experience.
2+ years industry experience working in Data Science, ML engineering, Data Analyst or similar roles
Expertise in statistical modelling and machine learning theory
Proficient in SQL & Python (including building packages and test suites)
Experience building and deploying models to production environments (CI, infrastructure as code, containerisation)
Strong Computer Science fundamentals in data structures, problem solving, algorithm design, and complexity analysis
Exposure to Git and Atlassian product suite (Jira, Confluence)
Team player capable of working collaboratively as well as individually
A structured approach to building, testing, and iterating on statistical models
Data handling skills, including cleaning, analysing, and interpreting large data sets, and an intuitive sense for identifying when the data is incorrect or inconsistent.
Excellent communication skills
Strong attention to detail.
The REA experience
The physical, mental, emotional and financial health of our people is something we’ll never stop caring about. This is a place to learn and grow.
Some of our Perks & Benefits include:
Our commitment to Diversity, Equity, and Inclusion
We are committed to providing a working environment that embraces and values diversity, equity and inclusion. We believe teams with diverse ideas and experiences are more creative, more effective and fuel disruptive thinking. If you've got the skills, dedication and enthusiasm to learn but don't necessarily meet every single point on the job description, please still get in touch.
Join our Talent Neighbourhood
Keen to be part of REA but didn't find a perfect match with this opportunity? Perhaps the timing isn't right? You should join our Talent Neighbourhood!
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