Senior Data Scientist (Business Intelligence Centre)

The Data Scientist is responsible for developing predictive models, optimisation and analytical solutions across a broad range of business problems including demand forecasting, price and elasticity modelling, customer and store segmentation, and AI/LLM-based solutions that enable data-driven decisions across commercial and operational functions. This role bridges business needs and advanced analytics, combining strong statistical, machine-learning, and data engineering skills with the ability to source diverse data, translate complex results into clear insights, and deliver production-ready solutions. The successful candidate will be adept at understanding business requirements, building models end-to-end, telling compelling data stories, and delivering high-impact solutions on time.

Responsibilities Modelling & Optimisation • Design, develop, and deploy predictive and machine-learning models across areas such as demand forecasting, price and elasticity modelling, price/assortment optimisation, and recommendation • Build customer and store segmentation, clustering, and entity-matching / item-mapping solutions to support commercial and marketing decisions • Frame business problems as data science problems, selecting appropriate methods and validation approaches to deliver reliable, production-ready outcomes • Continuously evaluate and improve model performance, accuracy, and business impact over time

AI & Advanced Analytics • Apply NLP and LLM/Generative AI techniques to use cases such as text classification, sentiment/voice-of-customer analysis, data mapping, and RAG or text-to-SQL applications • Prototype and evaluate emerging AI approaches, turning promising experiments into practical business solution.

Data, Insights & Data Sourcing • Source, acquire, and integrate data from internal systems, third-party providers, and external sources (e.g. web scraping, APIs, public datasets) • Explore, clean, and transform large datasets to prepare high-quality features; ensure data quality, consistency, and integrity • Identify trends, patterns, and opportunities in data — including external factors — and proactively surface insights that drive business value

Delivery & Productionisation • Build and maintain data pipelines and scheduled jobs (e.g. on Databricks) to run models and analytics reliably in production • Deliver results through dashboards, reports, and applications, and maintain clear documentation of models, data, and methodologies

Business Partnering • Engage with business stakeholders to understand objectives, gather requirements, and translate them into data science solutions • Communicate complex results clearly to technical and non-technical audiences, and manage timelines and deliverables to agreed success criteria