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# Customer Loyalty/Transaction Lead Data Scientist

- **Company:** NES Fircroft Engineering Services Limited
- **Location:** UK (Remote available)
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Microsoft Azure, Python (Programming Language), Power BI, Transaction Data, Sql Optimization, Apache Spark, Model Validation, Git, Pyspark, Databricks
- **Published:** October 30, 2026
- **Apply:** https://www.careerboard.com/gb/en/find-jobs-in-United-Kingdom/-8A175B0411FA69E172/

## About the Role

Must be eligible to work in the UK
Start ASAP
40 hrs week

Must Have: Advanced SQL for high-volume customer, payment, loyalty and transaction analysis. Strong Python for statistical analysis, matching, weighting, cohort analysis and reproducible data products. Hands-on Databricks/Spark or PySpark experience for notebooks, Delta tables, workflows and scheduled analytical pipelines. Working proficiency with Git/Azure DevOps, pull requests and controlled production changes. Strong data-validation, reconciliation and audit-trail practices.

Nice to have: Loyalty economics, retail fuel/convenience or CRM-domain experience. Power BI or certified reporting-layer experience. Production pipeline design beyond analytical notebooks, including Delta MERGE, idempotency and incremental processing. Experimentation or uplift-model validation experience. BNA or customer-decisioning exposure.

MUST HAVE high-volume customer, payment, loyalty and transaction analysis

## Description

Lead Data Scientist - Customer Intelligence, Incrementality & Value Measurement Primary focus: SCI/customer analytics, incrementality, CLV/RFM, customer and margin reconciliation, dampening methodology, scenario planning and multi-market value measurement. The role owns the analytical bridge between SCI customer and transaction data, CLV/RFM segmentation, incrementality methodology, Finance margin assumptions and leadership-facing value estimates. It must be able to determine not only the result, but why sources differ, whether the estimate is methodologically defensible, and how it can be reproduced and operationalised.