Payments Data Analyst

About the role

Mayflower is seeking a Payments Data Analyst to turn transaction data into actionable insights that improve the performance of its global payment infrastructure. The role works closely with Payments, Product, Finance, Risk and Data teams to boost success rates, lower costs and enhance the customer payment experience.

Key responsibilities

• Monitor and optimise payment performance metrics such as approval rates, decline patterns, 3‑DS flows, timeouts, retries and routing rules.
• Analyse fraud‑related declines, collaborate on rule tuning and adjust risk thresholds.
• Conduct deep‑dive analyses across card schemes, PSPs, BINs, countries and payment methods to uncover trends and anomalies.
• Translate findings into concrete actions – updating routing logic, cascading strategies, risk rules and operational configurations.
• Maintain continuous monitoring through daily health checks, anomaly detection and alerting on key metrics.
• Build and maintain dashboards and monitoring tools using Metabase, Amplitude or Tableau.
• Prepare clear analytics reports for internal stakeholders and senior leadership.
• Evaluate the impact of new features, routing changes, ML models and product experiments, providing data‑backed recommendations.
• Partner with Product, Data, ML, Payments Operations and Fraud teams to design and implement end‑to‑end improvements.

Required profile

• Strong analytical mindset with a solid understanding of payment flows, decline codes and 3‑DS processes.
• Experience working in fast‑moving, operational environments and a proactive ownership attitude.
• Ability to translate complex data into clear, actionable recommendations.
• Based in Cyprus or willing to relocate.

Required skills

• Advanced SQL (including window functions) and familiarity with NoSQL concepts.
• Proficiency in Python’s data‑science stack (Jupyter Notebook, Pandas).
• Experience with analytics and visualization tools such as Metabase, Amplitude and Tableau.
• Understanding of PSP behaviour, routing, tokens and basic fraud concepts (chargebacks, risk signals).