Senior Data Scientist

Role overview

Data science and product delivery

• Design, develop, deploy, and evaluate statistical, AI, predictive-analytics, and machine-learning solutions for operational challenges.
• Explore complex datasets for trends and actionable insights; communicate findings to technical and non-technical stakeholders.
• Manage datasets across staging, modelling, analytics, and presentation layers, supporting data quality, governance, and reproducibility.
• Collaborate with Product Owners and Business Analysts to translate user needs into data products supporting cyber capability requirements.

Platform and delivery

• Develop production-quality Python and SQL code; use Git source control and branch-management practices across deployment environments.
• Use Spark and PySpark for large-scale data processing and develop machine-learning workflows in MLOps environments.
• Use tools such as MLflow, VS Code, JupyterHub, Apache Iceberg, and Trino for model development, experimentation, deployment, and data analysis.
• Participate in Agile ceremonies, produce technical documentation, share knowledge, and improve data-science and MLOps practices.

Essential experience and skills

• Typically bring at least three years of relevant professional experience; candidates with less experience and strong evidence of capability may also be considered.
• Strong Python and SQL skills, including development of production-quality statistical, AI, and machine-learning solutions.
• Experience with large structured and unstructured datasets, data preparation, exploratory analysis, feature engineering, and model validation.
• Practical experience with Git, Agile delivery, collaborative software-development workflows, and distributed data-processing or MLOps tools such as Spark, PySpark, MLflow, or JupyterHub.
• Understand data-product lifecycles, data quality, governance, reproducibility, and auditable workflows.
• Translate operational or business needs into solutions; communicate clearly and collaborate effectively, with problem-solving, documentation, and critical-thinking skills.

Desirable experience

• Advanced statistical modelling, experimental design, forecasting, anomaly detection, model optimisation, or explainability.
• Machine-learning model deployment and operations, including monitoring, drift detection, and performance management.
• Awareness of lakehouse or enterprise data platforms such as Trino and Apache Iceberg.
• Experience in NLP, large language models, generative AI, deep learning, graph analytics, responsible AI, or retrieval-augmented generation.