Lead Data Scientist: Customer Intelligence & Incrementality

NES Group Ltd; City Of London; Vor Ort; GBP 110.000 - 170.000; Vollzeit

Zusammenfassung

NES Fircroft (NES) is seeking a Lead Data Scientist to advance customer intelligence, value measurement, and incrementality across markets. You will drive segmentation, CLV modelling, and robust analytics frameworks to support strategic decisions and performance insights.

You will partner with Finance and stakeholders to ensure reproducible value estimates while building scalable pipelines in Databricks and Python. The role requires leadership and strong data governance practices.

Qualifikationen

• Proficiency in SQL for large-scale analytics across customer and loyalty data.
• Strong Python skills for statistical modelling, cohort analysis, weighting and matching.
• Experience building automated data workflows and data quality controls.

Aufgaben

• Lead customer analytics initiatives including CLV, RFM segmentation, incrementality measurement, and scenario planning.
• Reconcile customer, transaction and margin data to produce trusted value estimates.
• Develop and maintain analytical frameworks to explain performance drivers and data discrepancies.
• Partner with Finance and stakeholders to validate methodologies and ensure reproducibility of value estimates.
• Build scalable analytics pipelines using Databricks, Spark, and Python.
• Establish data validation, audit, and reconciliation practices to support decision-making.

Kenntnisse

Advanced SQL

Strong Python (statistical modelling)

Data quality & reconciliation

Reproducible analytics practices

Tools

Databricks

Spark/PySpark

Delta tables

Git

Azure DevOps

Jobbeschreibung

NES Fircroft (NES) is seeking a Lead Data Scientist to advance customer intelligence, value measurement, and incrementality across markets. You will drive segmentation, CLV modelling, and robust analytics frameworks to support strategic decisions and performance insights.

You will partner with Finance and stakeholders to ensure reproducible value estimates while building scalable pipelines in Databricks and Python. The role requires leadership and strong data governance practices.