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Data Analyst - Customer Intelligence

Data Science & Analytics

To provide dedicated analytical capability within Customer Insights, connecting customer research, CRM data, product usage data and business performance metrics into clear, actionable insight. This role is designed to build future data science and AI capability while strengthening day-to-day reporting, dashboarding and evidence-based decision making.

You will be responsible for turning customer, commercial and operational data into practical insight that improves customer understanding, product decisions, retention, engagement and business performance. You will work closely with customer researchers and cross-functional partners to bring together quantitative and qualitative evidence, helping the business move towards a more connected view of customer needs, behaviours and outcomes. You will have exposure to data science, automation, AI-ready analytics, cloud environments and customer data architecture.

Key Responsibilities:

1. Analytics and Insight Generation

• Analyse customer, product usage, commercial, Salesforce, survey and operational data to identify trends, opportunities and areas of risk.
• Support the development of insight that helps the business understand customer behaviour, engagement, retention, product adoption and customer experience.
• Conduct ad hoc analysis to support strategic projects, customer insight initiatives and leadership decision-making.
• Work with Customer Insights colleagues to combine quantitative analysis with customer research findings and create clear recommendations.

2. Data Management and Reporting

• Extract, query, clean and manipulate data from multiple sources using SQL, Python and Excel.
• Support the maintenance and improvement of regular reports, dashboards and insight packs.
• Help define and track customer and business measures, including NPS, customer engagement, retention, product adoption and usage metrics.
• Support data validation and quality checks so reporting and recommendations are based on consistent and reliable data.

3. Data Science, Automation and AI Readiness

• Use Python to automate recurring analysis, reporting processes and data preparation tasks.
• Support statistical analysis, modelling and predictive analytics where appropriate.
• Contribute to proof-of-concept work linked to AI, data science and advanced customer analytics.
• Help identify opportunities to use automation and emerging analytical methods to improve the speed, quality and usefulness of insight.

4. Data Visualisation and Storytelling

• Create clear dashboards and visualisations in Power BI that help teams understand customer and business performance.
• Present analytical findings in a simple, practical way for both technical and non-technical audiences.
• Support the creation of leadership-ready insight summaries, packs and presentations.
• Help translate data into a clear story: what is happening, why it matters and what action should be considered.

5. Collaboration and Business Partnership

• Work cross functionally with key stakeholders, helping to turn business questions into analytical approaches and measurable outputs.
• Share learning openly and contribute to a more joined-up view of customer data and customer evidence.
• Support a culture of evidence-based decision making by helping teams use data with confidence.

Skills and Experience:

Essential

• Bachelor's Degree holder
• Strong analytical and problem-solving skills, with the ability to work with complex datasets and generate practical insight.
• Strong data science and programming skills, with proficiency in Python for data analysis, modelling, automation and statistical applications.
• Experience in data manipulation, querying and analysis using SQL.
• Skilled in data visualisation and presentation of analytical findings using Power BI.
• Competent in Microsoft Excel for data analysis, reporting and business insight generation.
• Ability to communicate analytical findings clearly to business stakeholders.
• Strong attention to detail, especially around data quality, accuracy and interpretation.
• Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.

Desirable

• Experience using Salesforce and understanding of how CRM data can support analytics and decision-making.
• Understanding of customer insight, market research, customer experience or NPS measurement.
• Working knowledge of cloud computing concepts and cloud-based visual or analytical environments.
• Understanding of data architecture concepts, including data warehousing and data lakes.
• Exposure to cloud computing environments such as Microsoft Azure.
• Exposure to predictive analytics, machine learning, AI-enabled analytics or advanced statistical methods.
• Experience working with customer, commercial, subscription, product usage or behavioural datasets.

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