Responsibilities

• Own the measurement and analytics framework for our growing marketing & paid acquisition efforts, helping determine where and how we should deploy significant marketing budgets.
• Develop frameworks for understanding incrementality, attribution, CAC, LTV, ROAS, payback periods and channel efficiency, ensuring we optimise for genuine business impact rather than platform-reported performance.
• Design and analyse experiments across creative, audience, channel, bidding and landing-page strategies to identify what drives incremental user acquisition, engagement and revenue.
• Build models and analytical tooling to understand user quality and predict downstream value across different campaigns, channels, cohorts and acquisition sources.
• Partner closely with Growth and Marketing to turn complex data into clear recommendations on budget allocation, campaign strategy and where to scale or reduce spend.
• Combine advertising platform data with first-party product, behavioural and on-chain data to build a complete view of the acquisition funnel and customer lifecycle.
• Build foundational data models, dashboards and automated reporting that allow teams to understand marketing performance in real time.
• Identify opportunities to improve our marketing data infrastructure, tracking and eventing, working closely with Engineering to ensure we have reliable data from impression through to downstream product behaviour.
• Contribute to the wider Data Science function across product performance, experimentation, user behaviour, ecosystem analysis and competitive intelligence.

Requirements

• An experienced Data Scientist with significant exposure to paid acquisition, performance marketing or growth analytics within a scaled consumer technology business.
• Highly proficient in Python or R and SQL, with the ability to work independently across large and complex datasets.
• Deeply familiar with the challenges of measuring paid marketing performance, including attribution, incrementality, selection bias and platform-reported metrics.
• Experienced designing and analysing experiments, including geo tests, holdouts, A/B tests or other causal inference approaches used to measure marketing effectiveness.
• Comfortable working with metrics such as CAC, LTV, ROAS, retention, conversion and payback, and understand how those metrics interact across the customer lifecycle.
• Able to move beyond reporting what happened and determine why it happened, what will happen next and what we should do about it.
• Comfortable building data models and lightweight pipelines, ideally with tools such as dbt, Dagster and BigQuery.
• Experienced with data visualisation and dashboarding tools and capable of creating clear, decision-oriented reporting for non-technical stakeholders.
• High-agency and comfortable owning ambiguous, high-impact problems from initial question through to recommendation and implementation.
• An excellent communicator who can work closely with Marketing, Product and Engineering teams and influence decisions through data.

Preferred

• Experience working with large-scale paid acquisition budgets across channels such as Meta, Google, TikTok, X or other major advertising platforms.
• Experience developing or implementing marketing mix modelling, multi-touch attribution, incrementality testing or other advanced marketing measurement frameworks.
• Experience building LTV, propensity, conversion or user-quality models used to inform acquisition strategy or bidding decisions.
• Experience working with advertising platform APIs, mobile attribution platforms or customer data platforms.
• Experience operating within a high-growth consumer product, marketplace, fintech, gaming or social platform.
• Experience with machine learning methods applied to growth, marketing or user behaviour.
• Experience with crypto, blockchain or on-chain data, or a demonstrated interest in the space.
• Familiarity with data visualisation and modelling tools such as Omni.