Data Scientist (Python, SQL, Machine Learning, Production AI Models)

About the role

We are looking for a Data Scientist to support across 4-5 product squads working with government agencies to redesign workflows and build AI-enabled solutions. You will work on problems involving large volumes of operational data. Your role is to help teams move beyond rules-based automation by identifying patterns in data, detecting anomalous or higher-risk cases, and building models that help officers prioritise their attention. This is an applied role. You should be comfortable moving from exploratory analysis and modelling to building data pipelines and putting models into production.

Key responsibilities

• Analyse large operational datasets to identify patterns, anomalies, outliers and risk indicators.
• Develop approaches for anomaly detection, clustering, segmentation and risk scoring.
• Build supervised machine-learning models where historical outcomes or labelled cases are available.
• Perform feature engineering using structured and unstructured data.
• Design evaluation approaches to assess model performance, including precision, recall, false positives and false negatives.
• Work with domain experts to determine how analytical outputs should be incorporated into operational workflows.
• Build and maintain data-processing and feature pipelines to support modelling at scale.
• Develop production-ready model or scoring pipelines and integrate them with applications built by the forward-deployed teams.
• Monitor model performance, data quality and data drift after deployment.
• Conduct exploratory analysis to identify new opportunities for workflow redesign and automation.

About you

• Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics or Information Systems preferred.
• Experience working with large, messy operational or transactional datasets.
• Experience in applied data science, including statistics, feature engineering, machine learning and model evaluation.
• Experience with techniques such as anomaly detection, classification, clustering or risk modelling.
• Experience building data pipelines and taking models beyond experimentation into production environments.
• Strong proficiency in Python and SQL (essential).
• Able to translate a business or operational problem into an analytical problem and determine the appropriate modelling approach (essential).
• Familiarity with software engineering practices including Git, automated testing and APIs (essential).
• Comfortable assessing model trade-offs and explaining them clearly to non-technical stakeholders (essential).
• Familiarity with containers, CI/CD or other DevOps / ModelOps practices would be advantageous.