Marketing Machine Learning Engineer, Cleo

• If you’re driven by complex challenges that push your expertise, the chance to shape something truly transformative, and the potential to share in Cleo’s success as we scale, while growing alongside a company that’s scaling fast, this might be your perfect fit
• 3+ years in Data Science, with a focus on Marketing Science or a related field
• Strong background in statistical analysis, including Marketing Mix Modelling and Predictive LTV modelling
• Excellent knowledge of both Data Science (Python, SQL) and production tools (Airflow)
• Experience of shipping tested models which make predictions in batch or real time
• Proven ability to understand stakeholder problems and build models that get used for decision-making, including surfacing the results in tooling or dashboards

Desirable

• Machine Learning Engineers at Cleo work on building novel solutions to real-world problems
• This really does vary but could be: creating chatbots to coach our users around their financial health, creating classifiers to better understand transaction data or even optimising transactions within our payments platform
• Ultimately, we’re looking for a Machine Learning Engineer specialising in Marketing Science to become a cornerstone of our high-performing team
• As the first Machine Learning Engineer in this squad, you’ll harness data to unlock insights into channel performance, support to optimise marketing strategies, and drive our growth
• Working closely with our Director of Performance Marketing, Marketing Analytics Manager, and cross-functional teams, your work will directly impact how we attract, engage, and retain Cleo’s users
• Marketing Measurement: Building out some of and improving on Cleo’s marketing measurement toolkit, including Marketing Mix Models (MMM), attribution modelling, and incrementality testing to provide the team with clear insights into the drivers of overall and channel performance
• Lifetime Value Modelling: Develop and optimise predictive models that forecast user lifetime value, ensuring data-driven channel optimisation and decisions that maximise Return On Ad Spend
• Budget Allocation Tools: Create scalable, automated decision-support tooling that optimises the distribution of our marketing budget across channels. These tools will integrate data from multiple sources including MMM, incrementality tests, and our single source of truth (SSOT) data to maximise ROAS
• Marketing Lift Tests: Work with our performance marketing team and marketing platforms (Meta, Tiktok, Apple etc) to design and implement lift studies that enhance our understanding of channel performance & incrementality
• Brand Marketing Campaign Analysis: Develop causal inference models to provide deeper insights into the performance and uplift of our brand marketing campaigns on awareness and acquisition
• Operational Excellence: Continuously review and refine data processes, collection and tooling, collaborating with engineering to maximise efficiency