Senior Data Analyst: Customer Success & AI Analytics-Hybrid

EF Recruitment; Greater London; Hybrid; GBP 70,000 - 100,000; Full time

Benefits offered by this job

Hybrid working

Award winning work environment

Long contract with potential to extend

Job summary

EF Recruitment seeks an experienced Data Analyst to join a Global Customer Success Data Science team on a 9-month contract. You will collaborate with cross-functional teams to measure and improve customer success across multiple domains, building pipelines, dashboards and AI-enabled analytics.

Responsibilities include developing scalable data foundations, delivering deep-dive analyses, enabling experimentation, and storytelling with insights for both technical and non-technical stakeholders.

Qualifications

• 8+ years of data analytics or data science experience.
• Proficiency in SQL for data extraction, transformation and pipelines.
• Experience with dashboards and visualization tools (Tableau, Qlik or similar).

Responsibilities

• Build robust data foundations by collaborating with engineering to develop pipelines, improve data integrity, and define metrics for international markets.
• Develop scalable dashboards and reporting to monitor key performance metrics across the customer journey.
• Conduct deep-dive analytics to identify drivers of pain points and improve customer journeys.
• Leverage Gen AI to automate insights generation and support anomaly detection.
• Support experimentation and continuous improvement through A/B testing and evaluation of digital experiences.
• Communicate insights and trends clearly to technical and non-technical stakeholders to influence decisions.

Skills

SQL

Visualization (Tableau/QLik)

Python/R

Big data tools

Gen AI tools

A/B testing

Data storytelling

Tools

Snowflake

Databricks

Spark

Job description

EF Recruitment seeks an experienced Data Analyst to join a Global Customer Success Data Science team on a 9-month contract. You will collaborate with cross-functional teams to measure and improve customer success across multiple domains, building pipelines, dashboards and AI-enabled analytics.

Responsibilities include developing scalable data foundations, delivering deep-dive analyses, enabling experimentation, and storytelling with insights for both technical and non-technical stakeholders.