Data Scientist

Job purpose:

Our IT mission is to transform how data and AI are delivered to customers and colleagues around the world, with a focus on simplicity, trust, and measurable value. AI is central to this mission, providing tools and platforms for creating and operating secure, scalable data and AI solutions.

Reporting to the BI and Analytics Manager, the Data Scientist is a valued member of the Enterprise Analytics & AI Hub. This role develops and evaluates advanced analytics, machine learning, and generative AI solutions that help us understand business challenges, improve decisions, and create better customer and colleague experiences.

Using modern data science techniques and scalable AWS technologies, the Data Scientist connects business needs with reliable technical solutions. This role works closely with product, data engineering, software and platform engineering, security, governance, and domain experts to move ideas from discovery and experimentation into monitored production services.

The Data Scientist plays a vital role within the Enterprise Analytics & AI Hub, contributing to the team's core objectives:

• Turn ambiguous business questions into measurable data science problems, clear success measures, and evidence-based recommendations.

• Build trusted models, data products, and AI capabilities in Nexus (our internal platform) that colleagues can use safely and effectively.

• Improve the quality and usability of structured and unstructured data by making definitions, lineage, assumptions, and limitations clear.

• Establish evaluation, observability, and feedback practices that improve accuracy, reliability, latency, and cost over time.

• Foster a culture of responsible, data-informed experimentation by sharing insights, reusable approaches, and emerging techniques across the team.

Key Responsibilities

Key Responsibilities:

 Partner with product owners, domain experts, and stakeholders to translate business challenges into measurable analytical or AI outcomes, data requirements, and evaluation plans.

 Explore, prepare, and validate structured and unstructured data, establishing appropriate quality checks, feature definitions, lineage, and documented limitations.

 Design, develop, and validate statistical and machine learning solutions for tasks such as classification, regression, clustering, forecasting, segmentation, and optimization.

 Design experiments and apply statistical analysis, including A/B testing where appropriate, to measure impact, interpret uncertainty, and support sound decisions.

 Build and evaluate generative AI capabilities within Nexus (AWS stack), including prompt and context design, embeddings, retrieval-augmented generation, knowledge bases, and agent or tool behaviours.

 Write production-quality Python and SQL, and work with engineers to integrate successful solutions into scalable AWS services, data pipelines, and APIs.

 Apply MLOps and LLMOps practices, including version control, automated tests, CI/CD, reproducible deployment, monitoring, drift detection, and rollback.

 Define and maintain offline and live evaluation measures, using traces, token usage, cost, performance, and user feedback to improve solutions over time.

 Apply responsible AI, privacy, security, and data governance principles, with appropriate access controls, explainability, safety checks, and human oversight.

 Communicate insights, trade-offs, and recommendations clearly to technical and business audiences, and contribute reusable patterns, documentation, and learning across the team.