> Markdown version of [/jobs/ext/4016848-data-scientist](https://www.wearedevelopers.com/jobs/ext/4016848-data-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** NES Group Ltd - **Location:** Greater London, UK - **Contract:** Permanent contract - **Skills:** Computing Platforms, Microsoft Azure, Data Validation, Python (Programming Language), Power BI, Sql Optimization, Apache Spark, Git, Pyspark, Data Pipelines, Databricks - **Published:** October 30, 2026 - **Apply:** https://www.cv-library.co.uk/job/225779107/Data-Scientist ## About the Role * Advanced SQL for large-scale customer, loyalty, payment, and transaction analysis. * Strong Python skills for statistical modelling, cohort analysis, weighting, matching, and reproducible analytics. * Hands-on experience with Databricks, Spark/PySpark, Delta tables, and automated data workflows. * Proficiency with Git, Azure DevOps, and controlled production release processes. * Strong data quality, reconciliation, and audit-trail practices. Desirable Skills * Experience in loyalty programmes, CRM analytics, or retail fuel and convenience sectors. * Power BI or enterprise reporting platform expertise. * Production-grade data pipeline design, including incremental processing and Delta MERGE patterns. * Experimentation, uplift modelling, or incrementality validation experience. * Exposure to customer decisioning platforms or business needs analysis (BNA). ## Description * Lead customer analytics initiatives, including CLV, RFM segmentation, incrementality measurement, and scenario planning. * Reconcile customer, transaction, and margin data to create accurate and trusted value estimates. * Develop and maintain analytical frameworks that explain performance drivers and identify discrepancies between data sources. * Partner with Finance and business stakeholders to validate methodologies and ensure value estimates are reproducible and operationalised. * Build scalable analytical solutions and pipelines using Databricks, Spark, and Python. * Establish strong data validation, audit, and reconciliation practices to support decision-making.