Marketing Machine Learning Engineer

Summary

Join Cleo, a fast-growing fintech unicorn, as their first Machine Learning Engineer specializing in Marketing Science. You will be a cornerstone of a high-performing team, leveraging data to unlock insights into channel performance and optimize marketing strategies. This role involves building and improving marketing measurement tools, developing predictive models for user lifetime value, creating budget allocation tools, conducting marketing lift tests, and analyzing brand marketing campaigns. You will work closely with the Director of Performance Marketing and other cross-functional teams to directly impact user acquisition, engagement, and retention. Cleo offers a competitive compensation package, flexible work arrangements, and a supportive environment for growth and development.

Requirements

• 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

Responsibilities

• Build out some of and improve 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
• Develop and optimise predictive models that forecast user lifetime value, ensuring data-driven channel optimisation and decisions that maximise Return On Ad Spend
• 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
• 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
• Develop causal inference models to provide deeper insights into the performance and uplift of our brand marketing campaigns on awareness and acquisition
• Continuously review and refine data processes, collection and tooling, collaborating with engineering to maximise efficiency

Preferred Qualifications

• A proactive approach to learning new technologies and stepping outside your comfort zone, whether building data pipelines, or integrating data sources
• Experience with B2C subscription business models
• A keen interest in leveraging advanced analytics to drive financial technology innovations

Benefits

• A competitive compensation package (base + equity) with bi-annual reviews, aligned to our quarterly OKR planning cycles
• Work at one of the fastest-growing tech startups, backed by top VC firms, Balderton & EQT Ventures
• A clear progression plan
• Flexibility
• Work where you work best. We’re a globally distributed team. If you live in London we have a hybrid approach, we’d love you to spend one day a week or more in our beautiful office. If you’re outside of London, we’ll encourage you to spend a couple of days with us a few times per year. And we’ll cover your travel costs, naturally
• Company-wide performance reviews every 6 months
• Generous pay increases for high-performing team members
• Equity top-ups for team members getting promoted
• 25 days annual leave a year + public holidays (+ an additional day for every year you spend at Cleo, up to 30 days)
• 6% employer-matched pension in the UK
• Private Medical Insurance via Vitality, dental cover, and life assurance
• Enhanced parental leave
• 1 month paid sabbatical after 4 years at Cleo
• Regular socials and activities, online and in-person
• We'll pay for your OpenAI subscription
• Online mental health support via Spill
• Workplace Nursery Scheme