At SiteMinder, we believe our success comes from our people. We build diverse, inclusive teams where different voices, backgrounds and perspectives are valued. Our differences drive innovation for our customers, and our people bring the ideas, expertise and energy that help us keep moving forward. We’re better together.
What we do...
Right now, somewhere in the world, a hotel is taking a booking or increasing their revenue through technology you could help build. SiteMinder is the world’s leading hotel commerce platform. For more than 20 years, we’ve helped hotels attract guests, maximise revenue, forecast demand and perform at their best. Today, our platform supports 56,000+ hotels in 150+ countries, connects to 3,000+ partners and powers more than 140m reservations and AU$85 billion+ in hotel revenue each year.
That’s the kind of impact your work can have here. You’ll solve meaningful challenges with AI, simplify complexity and build solutions that help hotels perform. Supported by clear purpose, a strong platform and teams who care about what they’re building, you’ll have the opportunity to shape the future of an industry that never stands still.
About the Data Scientist role…
As a Data Scientist, you will play a pivotal role in building and scaling machine learning solutions that drive product intelligence and data-informed decision-making across SiteMinder. You will work closely with Principal Data Scientists and the Core Data Lab team to develop, validate, and productionise models that deliver real business impact. In collaboration with Engineering, you will focus on integrating models into products and tackling complex data science challenges related to prediction, recommendation, and optimisation.
What you’ll do…
Design and develop end-to-end ML solutions — from data exploration and feature engineering to model training, validation, and deployment.
Collaborate cross-functionally with engineers, analysts, and product teams to integrate predictive and recommendation models into customer-facing and internal applications.
Implement scalable ML pipelines using Databricks, PySpark, and Delta Lake, ensuring reproducibility, performance, and maintainability.
Run controlled experiments (A/B tests, uplift modelling, causal inference) to measure model performance and quantify business impact.
Operationalise models through CI/CD and MLOps best practices, including model versioning, monitoring, retraining strategies, and governance.
Monitor production systems for drift, performance degradation, and anomalies, applying explainability and fairness techniques where needed.
Contribute to the development of feature stores and reusable data assets to accelerate experimentation and deployment cycles.
Stay current with emerging trends in ML, MLOps, and cloud data technologies to continuously improve model accuracy, scalability, and efficiency.
What you have…
Extensive hands-on experience applying machine learning and statistical modelling in production or product-oriented environments.
Proven understanding of the full spectrum of ML techniques — from traditional models (linear/logistic regression, tree-based methods, ensemble learning) to modern deep learning architectures (CNNs, RNNs, transformers, graph neural networks, diffusion and foundation models).
Demonstrated ability to design scalable ML pipelines and automate workflows with MLOps tools (MLflow, Kubeflow, Databricks ML runtime, AWS Sagemaker, or AWS Bedrock).
Preferred experience in Python, with proficiency in Scikit-learn, Autogluone, PyTorch or TensorFlow, and PySpark MLlib.
Familiarity with retrieval-augmented generation (RAG) and fine-tuning of large language models is a plus.
Proficiency in SQL and distributed data frameworks, with experience in feature engineering at scale.
Nice to Have
Familiarity with real-time ML applications, such as online learning, streaming inference, or live recommendations.
Exposure to forecasting, anomaly detection, or probabilistic modelling in production systems.
Experience contributing to open-source projects, writing technical blogs, or presenting at data science conferences.
Interest in continuous learning and keeping up with cutting-edge AI research (e.g., foundation models, self-supervised learning, model compression).
Our Perks & Benefits…
- Mental health and well-being initiatives
- Generous parental (including secondary) leave policy
- Flexibility to work in a Hybrid model (2-3 days in-office)
- Paid birthday, study and volunteering leave every year
- Sponsored social clubs, team events, and celebrations
- Employee Resource Groups (ERG) to help you connect and get involved
- Investment in your personal growth offering training for your advancement
Think this role sounds like you? If so, we’d love to hear from you. Please send us your resume and our Talent Acquisition team will be in touch.
When you apply, feel free to share your pronouns and let us know if you need any adjustments during the interview process, we’re here to support you. We also warmly encourage applications from people from underrepresented backgrounds.