Senior Data Scientist Customer Experience

Location: London (2 days per week on site)Contract Length: 3 months initially, scope to extend Day Rate: £615 per day (Inside IR35)

A leading organisation is seeking an experienced Data Scientist to join their Customer Experience and Fraud Compensation team. This role will focus on developing and refining models that explain and predict customer outcomes - particularly in relation to complaints, satisfaction, and retention.

The successful candidate will help simplify and operationalise models to drive business decisions, enabling the company to better understand what drives positive customer experiences and identify opportunities for improvement.

• Build and enhance predictive models to assess customer satisfaction, loyalty, and post-complaint behaviour.

• Identify key drivers behind positive customer experiences and develop models to optimise service outcomes.

• Support the expansion and adaptation of existing models from international teams.

• Translate complex analytical outputs into clear, actionable insights for non-technical stakeholders.

• Develop visualisations and concise data stories to communicate findings to senior leaders.

• Ensure models remain robust, interpretable, and commercially relevant, avoiding unnecessary complexity.

• Proven experience as a Data Scientist within a customer, marketing, or consumer analytics environment.

• Strong proficiency in Python for data manipulation, analysis, and model development.

• Experience working with Vertex AI

• Solid understanding of predictive modelling, causal inference, optimisation, or uplift modelling.

• Demonstrated ability to simplify technical work for senior business audiences.

• Collaborative mindset with the ability to take on feedback and iterate effectively.

The position involves close collaboration with senior stakeholders across Fraud, Customer Service, and Analytics teams. The successful candidate must be comfortable engaging with senior leaders and presenting findings that influence operational and strategic decisions.