Data Scientist

Sage City; Newcastle upon Tyne; On-site; GBP 60,000 - 90,000; Full time

Job summary

Sage City is seeking a Data Scientist to translate business questions into data-driven AI solutions. You will design, validate, and deploy ML models within the Nexus platform, collaborating with product, data engineering, software and security teams to create reliable, scalable services on AWS.

Applicants should have strong Python/SQL skills, experience with variable data types, and a responsible AI mindset.

Qualifications

• Turn business questions into measurable data science problems with clear success measures.
• Build trusted models and data products usable by colleagues.
• Apply responsible AI, privacy, security, and governance principles.

Responsibilities

• Design, develop, and validate ML/AI solutions for classification, regression, forecasting, and clustering.
• Write production-quality Python/SQL and integrate into AWS services and data pipelines.
• Define experiments, perform A/B testing, and monitor model performance and cost.

Skills

Python

SQL

AWS

ML/AI

Generative AI

Data analysis

Experiment design

Education

Bachelor's degree in CS/Stats or related

Tools

Nexus platform

CI/CD tooling

Python tooling (pandas, scikit-learn)

Job description

Job Description 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.