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Salary- Join a growing Machine Learning and data science team
Charities - We have long-term partnerships with charities working to address homelessness.
We're REA
REA Group (https://www.rea-group.com/about-us/about-rea-group/) is not your average digital business. From humble beginnings in a garage in Melbourne's east in 1995, we have grown into a leading global digital business, specialising in property.
With bold and ambitious goals, we are 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 be named in Australia's “Top 5” Best Workplaces two years in a row, as well as being recognised as a Best Workplace for Women.
Where the team fits in
The ML Engineer sits in the Data Science team within Consumer Experience, which sits within the broader Product & Audience group, headed up by Melina Cruikshank.
The Data Science teams' mission is to propel REA's strategy through data and data science, driving largest & most engaged audiences, unparalleled data insights, and superior customer value. We combine our extensive behavioural data with property, listings, content and market data (and more) to understand our audience, customers and the market. These rich insights are then used to provide consumers personalised, relevant, contextual and timely marketing communications, content and experiences. We also use them to provide unique and powerful insights to our customers and partners, helping them better understand their audiences, leads, markets and product performance.
To do this, we develop, engineer, manage and maintain a range of Machine Learning models and processes, from classification models to deep learning algorithms, recommendation engines and more.
The Data Science team work closely with many teams across the business, including several teams in Consumer Product & Experience, Customer Product & Experience, PropTrack and Data Services, among others.
What the role is all about
The Machine Learning Engineer supports the business in the following ways:
- Support in the Design of, and then independently code, test, and deploy data science product pipelines using AWS SageMaker, Docker, Airflow, Python, BigQuery SQL, and other technologies.
Develop appropriate Machine Learning models to extract insights to help improve property valuations, predict user behaviour, and build personalised consumer experiences using recommendation engines.
Productionise new Machine Learning models and pipelines based on both structured and unstructured data (eg image and text).
Maintain and enhance existing MLOps capabilities in both the experimentation and productionisation of models, including the creation and maintenance of required cloud infrastructure.
Enable and build data and model visualisation tools for analytics and business support.
Support the design of data products in consultation with internal stakeholders
Lead and deliver project slices
Support the Senior and Lead ML engineers in ensuring quality and teamwork is being fostered in the Data Science team
Who we're looking for
- Our ideal candidate will have skills in the following areas:
Tertiary qualifications in ML or data engineering, software development, data science OR equivalent experience.
Competency in Python, SQL, and/or other relevant software/languages. We're happy for you to learn the particulars on the job, but you need to be able to code.
Competency with DevOps tools and techniques, particularly Docker, Git, GCP, AWS and CI/CD.
Competency with Machine Learning in a commercial production environment- experience with developing APIs, streaming data (eg Flink, Kafka), and generative AI is a plus.
Demonstrated ability to clean, synthesise, transform and manage data of any format and from various sources to provide value to the business.
Demonstrated knowledge and application of Machine Learning, statistical analysis and modelling on both structured and unstructured data (eg image and text).
Ability to communicate, collaborate, and contribute effectively across teams and with various business stakeholders.
Ability to present data, findings and recommendations to a high standard.
Motivation to learn - we are constantly learning together, mentoring each other and striving to do things better.
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. We're committed to your development - both professionally and personally. Your experience with us is something we take seriously.
We offer:
- A hybrid and flexible approach to working
Programs to support mental, emotional, financial and physical health & wellbeing
Our Because We Care program offers employees volunteering leave, community grants, matched payroll giving and our Community Café donates 100% of revenue to charity.
Hack Days so you can bring your big ideas to life
Performance recognition programs because hard work should never go unnoticed
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 - be it cultural and ethnic backgrounds, gender identity, disability, age, sexual orientation, or any other identity or lived experience. We know diverse teams are critical to maintaining our success and driving new business opportunities. 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.
If you like the sound of us, then we think you should apply today. We're proud to be Circle Back Initiative Employer and we commit to respond to every applicant. While we're looking at your application, and preparing to come back to you with an update, why not get to know us a bit more through our various social channels?
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