

Customer Data Analyst
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Customer Data Analyst on a 6-month contract, offering £400-£450 per day. It requires 3+ years of retail/FMCG analytics experience, expert SQL, and proficiency in Python or R. Hybrid work in London, with flexible start.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
-
💰 - Day rate
450
-
🗓️ - Date discovered
June 28, 2025
🕒 - Project duration
More than 6 months
-
🏝️ - Location type
Hybrid
-
📄 - Contract type
Outside IR35
-
🔒 - Security clearance
Unknown
-
📍 - Location detailed
London, England, United Kingdom
-
🧠 - Skills detailed
#Customer Segmentation #Storytelling #Redshift #Statistics #Tableau #Snowflake #BI (Business Intelligence) #Pandas #Data Science #"ETL (Extract #Transform #Load)" #A/B Testing #Python #BigQuery #CRM (Customer Relationship Management) #Airflow #SQL (Structured Query Language) #Data Engineering #Data Analysis #Data Extraction #Microsoft Power BI #R #TensorFlow #Cloud #dbt (data build tool)
Role description
Heading 1
Heading 2
Heading 3
Heading 4
Heading 5
Heading 6
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Block quote
Ordered list
- Item 1
- Item 2
- Item 3
Unordered list
- Item A
- Item B
- Item C
Bold text
Emphasis
Superscript
Subscript
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)
Desired Skills and Experience
8+ years in retail/FMCG customer insights and analytics
Built customer segmentation, CLV, and propensity models in Python/R
Designed and analysed A/B and multivariate tests for pricing and promotions
Developed ETL pipelines and real-time dashboards in Tableau and Power BI
Extensive SQL for large-scale data extraction and transformation
Presented insights and recommendations to marketing and executive teams
Operationalised consumer insights with data engineering and commercial partners