AI-Driven Data Analyst for Customer Success (Hybrid-London)

Redcat Digital; Greater London; Hybrid; GBP 92,000 - 138,000; Full time

Job summary

Redcat Digital in London is seeking a Data Analyst to join a global Customer Success Data Science team on an initial 10-month contract. The role focuses on turning data into actionable insight beyond dashboards, influencing decisions across CX, DX, and support.

You will work across international markets, collaborate with engineering on data pipelines, and help develop AI-native analytics, including Generative AI enabled insights and experimentation.

Qualifications

• 8+ years' experience in Data Analytics or Data Science.
• Strong SQL and Python/R skills.
• Experience with Tableau, Qlik or similar dashboards.

Responsibilities

• Analyse complex datasets using SQL and Python/R to identify trends, problems and opportunities.
• Build scalable dashboards, reporting and data foundations that enable teams to make better decisions.
• Conduct deep-dive and root-cause analysis into customer, digital and operational performance.
• Translate analysis into clear recommendations that influence business decisions and measurable outcomes.
• Support A/B testing and experimentation, analysing results and quantifying customer and business impact.
• Work closely with engineering teams on data pipelines, ETL, data quality and metric definitions.
• Use Generative AI and automation to improve insight generation, reporting and analytical workflows.
• Manage multiple projects across international markets and adapt to changing priorities.

Skills

SQL

Python/R

Dashboarding tools

ETL/pipelines

A/B testing

Generative AI

Tools

Tableau

Qlik

Snowflake

Databricks

Spark

Job description

Redcat Digital in London is seeking a Data Analyst to join a global Customer Success Data Science team on an initial 10-month contract. The role focuses on turning data into actionable insight beyond dashboards, influencing decisions across CX, DX, and support.

You will work across international markets, collaborate with engineering on data pipelines, and help develop AI-native analytics, including Generative AI enabled insights and experimentation.