Job Opportunities API

Help make the world more hospitable The hospitality industry is uniquely human, and it deserves technology that’s just as inspiring as the people behind it. At Mews, we’re transforming the industry with a platform that helps hotels run smarter, move faster and create better guest experiences. You’ll work with smart, curious people who care deeply about what they do. You’ll have autonomy and the trust to make good decisions and move quickly. And you’ll enjoy a real sense of purpose as you see the impact of what we’re building. If you’re motivated by ownership, curiosity and meaningful impact, and are driven to deliver consistent high performance, you’ll feel at home here. About the role Let's get into the specifics. It’s impossible to capture every nuance of a role – especially at a rapidly growing company like Mews – but if we had to distil it into a job description (which we do because this is a job description), it would be this: This role sits inside the FinCrime team at Mews Fintech, working at the intersection of fraud operations, data, and product. You will use data to improve how transaction monitoring works — finding the gaps in detection rules, reducing false positives, and helping the team focus its attention on the cases that actually matter. This is not a reporting role. The expectation is that you move from investigation to action: spot what is going wrong, understand why, and recommend practical changes that improve outcomes. You will work with SQL and Python across large datasets, partnering with FinCrime, Product, Compliance, and Data teams to improve controls, bring in new data sources, and support the development of internal scoring approaches and future machine learning use cases. The environment is fast-moving and not everything is perfectly defined — that is part of what makes the work interesting, and the right person will be energised by that rather than slowed by it. What you would do Review transaction monitoring and fraud rule performance to identify gaps, inefficiencies, and opportunities to improve detection Investigate trends, anomalies, and behavioural patterns across transactions, properties, and customer activity Improve how cases are prioritised by risk, reducing manual review volumes and increasing focus on high-risk activity Partner with FinCrime, Product, Compliance, and Data teams to improve controls and bring in new data sources Work with data engineers to support better pipelines, data quality checks, and more reliable monitoring inputs Support the development of internal scoring approaches and future machine learning use cases as the function evolves Translate complex findings into clear recommendations that improve fraud prevention while minimising friction for legitimate customers What you would bring Strong experience in fraud detection or transaction monitoring, with a track record of improving rules, controls, or detection approaches using data — not just describing what the data shows Advanced SQL skills and strong Python skills, with experience working across large datasets to identify patterns, outliers, and risk signals The ability to move from investigation to action: you do not stop at findings, you follow through to recommendations and work with stakeholders to implement them Comfort working cross-functionally with operational, technical, and compliance stakeholders You have integrated AI into how you work — using it consistently to get to conclusions faster, check your thinking, and improve the quality of your output Background in fintech, payments, buy now pay later, e-commerce, or transaction monitoring and fraud solution providers is strongly preferred Spain €41.500 — €54.100 EUR Czechia 716 500 Kč — 927 000 Kč CZK UK £45,000 — £59,500 GBP Pay Transparency at Mews Salary ranges are provided in good faith and reflect current ma