Data Scientist

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.

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.

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.

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• 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.

• 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).