Data Scientist

Job description

At SiteMinder we believe the individual contributions of our employees are what drive our success. That’s why we hire and encourage diverse teams that include and respect a variety of voices, identities, backgrounds, experiences and perspectives. Our diverse and inclusive culture enables our employees to bring their unique selves to work and be proud of doing so. It’s in our differences that we will keep revolutionising the way for our customers. We are better together! **What We Do…** We’re people who love technology but know that hoteliers just want things to be simple. So since 2006 we’ve been constantly innovating our world-leading hotel commerce platform to help accommodation owners find and book more guests online - quickly and simply. We’ve helped everyone from boutique hotels to big chains, enabling travellers to book igloos, cabins, castles, holiday parks, campsites, pubs, resorts, Airbnbs, and everything in between. And today, we’re the world’s leading open hotel commerce platform, supporting 56,000 hotels in 150+ countries - with over 140 million reservations processed by SiteMinder’s technology every year. **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 Does this job sound like you? If yes, we'd love for you to be part of our team! Please send a copy of your resume and our Talent Acquisition team will be in touch. When you apply, please tell us the pronouns you use and any adjustments you may need during the interview process. We encourage people from underrepresented groups to apply.