Senior Data Scientist - Payments Operations

Preply
BarcelonaPosted 27 March 2026

Job Description

Senior Data Scientist - Payments Operations WE POWER PEOPLE’S PROGRESS. At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalised learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it’s creating real impact. We’ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human-led, AI-enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries - and we’re only getting started. As a category-defining company, we’re shaping what the future of learning looks like at global scale. Every Preply lesson sparks change, fuels ambition, and drives progress that matters. Joining Preply means helping define the future of education at global scale, and building something that truly matters for millions of people, every day. MEET THE TEAM: At Preply, data is at the heart of every decision we make. We run hundreds of A/B tests to continually optimize our product, each with its own analytical and tracking challenges. The complexity of our subscription model, along with the unique dynamics of tutor-learner interactions, offers an exciting opportunity for those looking to make a real impact. As a Data Scientist for the Payments Operations (PayOps) team, you will play a crucial role in optimizing our payments ecosystem and driving our monetization strategy forward. Embedded within a cross-functional squad, you’ll collaborate closely with product managers, tech leads, designers, and other key stakeholders to deliver data-driven insights that shape business decisions. Your work will focus on analyzing and improving payment workflows, including checkouts, pay-ins, and payouts, to ensure a seamless experience for our students and tutors alike, as well as leading our Fraud management function. Our Data Team https://www.linkedin.com/posts/preply_data-team-uncovered-activity-7232342101730971652-3ate/?utm_source=share&utm_medium=member_desktop is dedicated to empowering top-quality decision-making. Do you want to know how? Visit our Tech Radar https://tech-radar.preply.com/ to learn about the technologies we use at Preply! WHAT YOU’LL BE DOING - Develop a deep understanding of the Payment Operations dynamics of our product, including user behavior and the economics of Preply’s marketplace. - Analyze data related to payment processes, including pay-ins and pay-outs, to uncover insights and identify opportunities for optimization. - Monitor transaction performance to detect and troubleshoot anomalies in payment flows (e.g., failed transactions, latency issues, fraud patterns). - Lead systematic analysis of fraud and risk patterns, including regular review, cleaning, and optimization of existing risk rules, and the development of data-driven, smart rules that go beyond static or manual blocks. - Act as a key data partner to ML Platform and AI teams, bridging Product, Payouts, and Payment Operations with advanced analytics and modeling capabilities, ensuring data science fully complements product and operational ownership in payments and fraud. - Quantify and model the impact of new product features and initiatives, identifying growth opportunities and contributing to the prioritization of our product roadmap. - Help define key performance indicators, tracking events, and engagement metrics that align with business goals and product improvements. - Design, execute, and evaluate large-scale experiments to test new ideas and measure their effectiveness in driving business outcomes. - Build strong relationships with data and technical leaders to foster collaboration and drive cross-team initiatives. WHAT YOU NEED TO SUCCEED - At least 4 years of experience in fraud management and payment operations analytics, including ML‑based fraud systems, pattern analysis, rule management, and product optimization through experimentation. ... (truncated, view full listing at source)
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