Marketing Machine Learning Engineer

At Cleo, were not just building another fintech app. Were embarking on a mission to fundamentally change humanitys relationship with money. Imagine a world where everyone, regardless of background or income, has access to a hyper-intelligent financial advisor in their pocket. Thats the future were creating.

Cleo is a rare success story: a profitable, fast-growing unicorn with over $200 million in ARR and growing over 2x year-over-year. This isnt just a job; its a chance to join a team of brilliant, driven individuals who are passionate about making a real difference. We have an exceptionally high bar for talent, seeking individuals who are not only at the top of their field but also embody our culture of collaboration and positive impact.

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.

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.

What You’ll Be Doing

• 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.

What you’ll need

• 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.

Nice to have

• 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.

What do you get for all your hard work?

• A competitive compensation package (base + equity) with bi-annual reviews, aligned to our quarterly OKR planning cycles.You can view our public progression framework and salary bandings here:https://cleo-ai.progressionapp.com/
• Work at one of the fastest-growing tech startups, backed by top VC firms, Balderton & EQT Ventures.
• A clear progression plan.We want you to keep growing. That means trying new things, leading others, challenging the status quo and owning your impact. Always with our complete support.
• Flexibility.We can’t fight for the world’s financial health if we’re not healthy ourselves. We work with everyone to make sure they have the balance they need to do their best work.
• 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.
• Other benefits:
• 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.
• Well pay for your OpenAI subscription.
• Online mental health support via Spill.
• Workplace Nursery Scheme.
• And many more!
• We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds.If there’s anything we can do to accommodate your specific situation, please let us know.

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