Data Scientist

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.

● Operationalize 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).