Responsibilities
Lead the creation of recurring and ad-hoc analyses: consumer behaviour, funnel performance, channel efficiency, journey optimisation opportunities, market trends, competitive benchmarking.
Coordinate development and maintenance of advanced models (e.g., churn prediction, LTV forecasting, propensity scoring, recommendation algorithms), employing AI and machine learning techniques.
Develop and implement experimentation frameworks (A/B testing, multivariate tests) in collaboration with activation teams.
Scout and evaluate emerging analytics tools that use artificial intelligence, translating capabilities into scalable, business-ready use cases.
Synthesise findings into compelling presentations, reports, and recommendations for senior leadership and marketing peers/teams.
Proactively identify and investigate growth levers or risks through exploratory analysis, including opportunities to improve the customer journey and channel performance insights.
Support strategic planning cycles with data-backed scenarios and forecasts.
Partner tightly with the Data Product Manager on prioritisation and productisation of analytical features, including AI-powered capabilities.
Collaborate with the Data Platform Manager on data improvements (via DBS and D&A) that unlock new analytical possibilities (e.g., enriched event data, cross-device identifiers, AI/ML-ready pipelines).
Integrate insights into campaigns, support cross-functional initiatives, and lead analytical governance, team development, and guidelines (incl. self-service, rigour, and capability building).
Serve as the go-to expert for growth-related questions, balancing depth of analysis with speed to deliver both quick-win insights and longer-term strategic studies.
Translate complex analytical findings into actionable, non-technical narratives for executive and marketing stakeholders.
Requirements
Minimum 8+ years in data analytics, business intelligence, data science, or intelligence leadership roles, with at least 3–5 years in a management position.
Proven track record to translate analytics into growth impact in consumer-facing businesses (e.g., e-commerce, digital marketing, subscription services).
Extensive experience with growth/marketing analytics (attribution, cohort analysis, customer journey mapping, personalization, channel performance analysis).
Ability to enable evidence-based decision-making and foster a culture of analytical rigour, experimentation, and continuous learning.
Background leading experimentation programs and influencing business decisions through data.
Prior work in matrixed organizations with reliance on centralized data infrastructure and martech stacks.
Hands-on analytical experience combined with strong people and collaborator leadership experience.
Extensive global exposure across diverse markets and regulatory environments, with deep experience in consumer-facing industries and growth optimization methodologies.
Expertise in statistical analysis, hypothesis testing, predictive modelling, and proficiency in tools/languages (advanced SQL, Python/R – pandas, scikit-learn, statsmodels).
Experience with experimentation platforms, causal inference methods, data visualization (Power BI), and communicating insights to executive audiences; proven understanding of data flows and martech ecosystems.
Familiarity with growth data concepts (multi-touch attribution, incremental, RFM), deep expertise in growth metrics (CAC, LTV, retention, funnels), knowledge of data privacy, and strong awareness of AI/ML-driven analytics with a relevant graduate degree.
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Lead the creation of recurring and ad-hoc analyses: consumer behaviour, funnel performance, channel efficiency, journey optimisation opportunities, market trends, competitive benchmarking.
Coordinate development and maintenance of advanced models (e.g., churn prediction, LTV forecasting, propensity scoring, recommendation algorithms), employing AI and machine learning techniques.
Develop and implement experimentation frameworks (A/B testing, multivariate tests) in collaboration with activation teams.
Scout and evaluate emerging analytics tools that use artificial intelligence, translating capabilities into scalable, business-ready use cases.
Synthesise findings into compelling presentations, reports, and recommendations for senior leadership and marketing peers/teams.
Proactively identify and investigate growth levers or risks through exploratory analysis, including opportunities to improve the customer journey and channel performance insights.
Support strategic planning cycles with data-backed scenarios and forecasts.
Partner tightly with the Data Product Manager on prioritisation and productisation of analytical features, including AI-powered capabilities.
Collaborate with the Data Platform Manager on data improvements (via DBS and D&A) that unlock new analytical possibilities (e.g., enriched event data, cross-device identifiers, AI/ML-ready pipelines).
Integrate insights into campaigns, support cross-functional initiatives, and lead analytical governance, team development, and guidelines (incl. self-service, rigour, and capability building).
Serve as the go-to expert for growth-related questions, balancing depth of analysis with speed to deliver both quick-win insights and longer-term strategic studies.
Translate complex analytical findings into actionable, non-technical narratives for executive and marketing stakeholders.
Requirements
Minimum 8+ years in data analytics, business intelligence, data science, or intelligence leadership roles, with at least 3–5 years in a management position.
Proven track record to translate analytics into growth impact in consumer-facing businesses (e.g., e-commerce, digital marketing, subscription services).
Extensive experience with growth/marketing analytics (attribution, cohort analysis, customer journey mapping, personalization, channel performance analysis).
Ability to enable evidence-based decision-making and foster a culture of analytical rigour, experimentation, and continuous learning.
Background leading experimentation programs and influencing business decisions through data.
Prior work in matrixed organizations with reliance on centralized data infrastructure and martech stacks.
Hands-on analytical experience combined with strong people and collaborator leadership experience.
Extensive global exposure across diverse markets and regulatory environments, with deep experience in consumer-facing industries and growth optimization methodologies.
Expertise in statistical analysis, hypothesis testing, predictive modelling, and proficiency in tools/languages (advanced SQL, Python/R – pandas, scikit-learn, statsmodels).
Experience with experimentation platforms, causal inference methods, data visualization (Power BI), and communicating insights to executive audiences; proven understanding of data flows and martech ecosystems.
Familiarity with growth data concepts (multi-touch attribution, incremental, RFM), deep expertise in growth metrics (CAC, LTV, retention, funnels), knowledge of data privacy, and strong awareness of AI/ML-driven analytics with a relevant graduate degree.
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