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Confused.com – a first insurance comparison site founded in 2002, part of the RVU group including Uswitch, Tempcover and money.co.uk. We work to simplify the process of sorting insurance, utilities and personal finances and are a cutting‑edge FinTech brand. We’re looking for a strategic analytical leader to drive business impact and help optimise our multi‑million pound marketing budget.

This is a hybrid role. You'll be expected to join us at one of our main hubs (London or Cardiff) approximately twice a month for key team meetings, workshops and planning sessions.

In this role you will quantify the effectiveness of our marketing strategy, partner with Marketing, Finance and leadership to optimise the marketing budget and shape the future of our growth. The analytics function is evolving from a reactive reporting team into a proactive, value‑generating engine – you will play a key architectural role, bringing causal inference expertise and improving team technical standards.

Reporting to the Head of Analytics, you will join our central Data Science team, a high‑impact group of specialists that functions as an internal centre of excellence.

• What is the true incremental impact of our TV advertising on car insurance sales?
• How should we reallocate our budget between different marketing channels to maximise ROI?
• What is the true impact of our promotional offers on customer acquisition and retention?
• Which customer segments are most responsive to our marketing efforts and why?

• Take ownership of a critical, high‑value, high‑visibility process that determines our ad spend.
• Design, build, and maintain advanced Marketing Mix Models to inform strategic budget allocation.
• Apply causal inference techniques to measure the incremental impact of specific marketing campaigns and initiatives.
• Partner with senior stakeholders to translate complex model outputs into clear, actionable recommendations for leadership.
• Champion and build greater team autonomy by designing robust, source‑controlled data pipelines for marketing data products.
• Mentor and up‑skill our wider analytics team on experimental design and causal inference, helping to level‑up the entire team.

• Expert‑level SQL and Python skills for large‑scale data analysis and model development.
• Deep understanding of causal inference methodologies (e.g. DiD, PSM/IPW) and advanced experimental design (A/B/n testing).
• Ideally, proven experience developing and deploying Marketing Mix Models in a commercial environment.
• Strong statistical foundation, ideally including Bayesian methods.
• Passion for building production‑quality, source‑controlled data products, with data engineering skills to own data pipelines.
• Exceptional stakeholder management and influence, translating complex quantitative concepts to senior, non‑technical audiences.
• Advocate for data‑driven decisions across multi‑disciplinary teams.
• Growth mindset and passion for mentoring, with a desire to raise the team’s technical standard.

• 10 % discretionary yearly bonus and yearly pay reviews.
• Fully remote working approach with up to 22 working days per year from anywhere.
• Employer matching pension contributions up to 7.5 %.
• One‑off £300 “Work from Home” budget.
• Excellent maternity, paternity, shared parental and adoption leave policy.
• 25 days holiday (increasing to 30 days) + 2 days “My Time” per year.
• Private medical cover, critical illness cover, dental plans, employee assistance programme.
• Free gym access and employee discounts programme.
• Healthy learning and training budget.
• Electric vehicle and cycle‑to‑work schemes.
• Regular events – team socials and company‑wide events with external speakers.

As a tech company striving for continuous improvement, we use Metaview during interview processes for note‑taking. This records and transcribes interviews, allowing interviewers to focus fully on the conversation. Participation is optional and can be opted out at any time.

Seniority level: Mid‑Senior

Employment type: Full‑time

Job function: Engineering and Information Technology