Customer Data Analyst

Job Title
Specialist Retail Customer Analyst

Logistics

• Contract length: 6 months

• Day rate: £400-£450 (Outside IR35)

• Location: Hybrid-2 days/week in London office

• Start date: Flexible (within the next month)

The Context

You'll embed within a leading retail/FMCG organisation that treats consumer insight as its competitive edge. Your analyses will shape loyalty programmes, promotional strategies and product assortments to maximise retention, basket size and lifetime value.

Role Overview
As a Specialist Retail Customer Analyst, you'll own end-to-end customer analytics: extracting and modelling sales and behavioural data, building dashboards and presenting actionable consumer insights to cross-functional teams.

Key Responsibilities

• Develop customer segmentation, CLV and propensity models to inform targeting and promotions

• Design and evaluate A/B tests and multivariate experiments on pricing, merchandising and UX

• Build automated ETL pipelines and maintain real-time dashboards (Tableau/Power BI) for sales, churn and engagement metrics

• Translate complex analyses into clear recommendations for marketing, e-commerce and executive stakeholders

• Partner with data engineering and commercial teams to operationalise insights

Must-Haves

• 3+ years' customer-insights or analytics experience in retail/FMCG

• Bachelor's or Master's in a quantitative discipline (e.g., Economics, Statistics, Data Science)

• Expert SQL skills and proficiency in Python or R for statistical modelling

• Hands-on dashboarding experience with Tableau or Power BI

• Demonstrable experience designing and analysing A/B tests

• Strong storytelling and stakeholder-management skills

• Full UK work authorization

Desirable

• Familiarity with cloud data platforms (Snowflake, Redshift, BigQuery) and ETL tools (dbt, Airflow)

• Experience with loyalty-programme analytics or CRM platforms

• Knowledge of machine-learning frameworks (scikit-learn, TensorFlow) for customer scoring

Technical Toolbox

• Data & modeling: SQL, Python/R, pandas, scikit-learn

• Dashboarding: Tableau or Power BI

• ETL & warehousing: dbt, Airflow, Snowflake/Redshift/BigQuery

• Experimentation: A/B testing platforms (Optimizely, VWO)