Overview
Lead the design and execution of experimentation frameworks, including geo-based and holdout incrementality testing, to rigorously validate marketing investment decisions at scale. Evolve the organisation’s Marketing Mix Modelling (MMM) capability, including model development, calibration, and translation into actionable budget allocation recommendations for senior leadership. Develop sophisticated statistical models such as LTV prediction, churn scoring, and audience segmentation that influence strategic decisions across Marketing, Finance, and Product. Architect and govern the marketing data layer, ensuring best practice across dbt modelling, data quality, and documentation. Define and lead cross-functional analytics initiatives that span teams and business units, bringing structure and analytical rigour to ambiguous, high-stakes commercial problems. Translate highly complex findings into compelling strategic narratives and executive-ready recommendations, enabling data-driven decisions at the leadership level. Partner with Finance (FP&A) to ensure marketing efficiency metrics, budget frameworks, and unit economics (CAC, LTV, Payback Period) are consistent with company-wide financial reporting and planning. Collaborate with Data Engineering and Marketing Technology to define and shape the long-term marketing data stack roadmap, ensuring tracking integrity, instrumentation standards, and tooling choices reflect best practice. Lead the team’s thinking on how AI tools and techniques can augment the analytics workflow, and act as a practical guide for marketing teams on where and how AI can meaningfully improve their ways of working.
Requirements
- 8+ years of experience in a similar role
- Deep expertise across Acquisition (Paid Search, Paid Social, SEO) and Retention (CRM, Lifecycle, Churn), with the ability to connect channel-level performance to business-wide outcomes
- Proven, hands-on experience building and deploying Marketing Mix Models (MMM) and incrementality testing frameworks (geo-based, holdout, synthetic control) in a real business context
- Proficiency in applied statistical modelling, such as LTV modeling, predictive scoring and causal inference techniques
- Advanced Python or R for statistical analysis, modelling, and automation, with production-quality code standards
- Practical experience applying AI tools to accelerate, enhance and automate analytical workflows
- Demonstrated experience architecting scalable data models using SQL and dbt within a modern data stack, including setting standards and reviewing others’ work
- Exceptional ability to distil complex technical findings into clear strategic narratives for non-technical stakeholders and senior leadership
- Experience working within a high-growth SaaS or digital business with an understanding of unit economics (CAC, LTV, Payback)
- Demonstrated experience mentoring and developing analysts including coaching on technical skills, analytical thinking and stakeholder engagement
- Exposure to product analytics or growth functions, with an ability to connect marketing and product data across the funnel
- Prior experience influencing tooling, vendor or data infrastructure decisions at an organisational level
#J-18808-Ljbffr…