Fraud & FinCrime Data Analyst

We are looking for a technical analyst to join our FinCrime team as one of the owners of our rules framework, KPI and activity tracking, and the continuous improvement of our detection capabilities.

You will identify gaps, propose solutions, and drive them to delivery alongside Product, Compliance, Network and Engineering, keeping us ahead of bad actors without hurting speed or user experience.

• Rules lifecycle: design, build, test, tune and retire fraud and AML/TF detection rules, from hypothesis through backtesting and deployment to performance review.
• KPI framework: define, build and maintain metrics for detection effectiveness, false positives, losses, alert volumes and operational productivity.
• Activity tracking: reporting on rule changes, alert handling and team throughput, with dashboards that stakeholders actually use.
• Detection improvement: proactively find new typologies, data signals and controls, and turn them into live rules or models.
• Build and improve detection logic: translate fraud and financial crime typologies into rules; use SQL and Python to backtest, simulate and measure impact before release.
• Monitor and analyze: track real-time and batch transaction data, spot anomalies and emerging attack patterns, and run deep-dives on loss drivers and rule performance.
• Drive automation: replace manual analysis and reporting with scalable pipelines, scheduled jobs and self-serve dashboards.
• Collaborate as a partner:
• Product and Engineering: define requirements, data needs and system enhancements.
• Compliance: make sure detection meets AML/TF, KYC and regulatory expectations, and support audits and partner due diligence.
• Network: align on Visa and Mastercard program requirements, thresholds and monitoring.
• Operations: use investigation feedback to refine rules and reduce noise.
• Support regulatory compliance: stay current on AML regulations, PSD2/SCA, and card scheme rules, and help embed them into controls.
• Take responsibility and escalate: document decisions and rule rationale clearly, and escalate material risks early.

First 3 months

• Understand our products, detection framework, rules engine and data landscape.
• Learn our KPIs, tooling and key fraud and AML patterns.
• Build relationships across Product, Compliance, Engineering and Operations.
• Take responsibility for a first set of rules and ship your first tuning improvements.

By 6 months

• Co-own the end-to-end rules lifecycle for your portfolio, with documented performance reviews.
• Launch a reliable KPI and activity tracking suite.
• Deliver your first automation that removes recurring manual work.

By 12 months

• Be a key contributor and owner for detection effectiveness and reporting.
• Deliver measurable gains in loss reduction, false positive rate or operational efficiency.
• Have introduced new detection approaches and automation that strengthen our compliance posture.

• Experience in fraud or financial crime analytics, ideally in fintech, payments or high-volume B2C.
• Proven hands-on experience with rule-based detection: designing, tuning and evaluating rules, with clear trade-off thinking (catch rate vs. false positives vs. customer friction).
• Advanced SQL and experience with modern data platforms and large-scale data processing (distributed frameworks such as Spark or similar).
• Python for analysis and automation (pandas, notebooks, scheduled workflows), including experience working with large datasets.
• Solid understanding of fraud typologies and AML/TF concepts, and awareness of emerging trends.
• Knowledge of Visa and Mastercard tools, data and monitoring programs (AVS, 3DS, chargeback and fraud programs).
• Practical knowledge of payment methods (cards, tokenization, Apple Pay, Google Pay, etc.).
• Experience building dashboards in a BI tool (Looker, Tableau, Power BI or similar).
• Ownership mindset: you spot problems, propose solutions and see them through without waiting to be asked, while working as part of a team.
• Clear communication and documentation, able to explain data-driven decisions to technical and non-technical stakeholders.
• Experience with rules engines or decisioning platforms and expression-based rule logic.
• Experience with advanced methods for anomaly detection (statistical, unsupervised or machine learning approaches) and applying them to fraud or financial crime use cases.
• Familiarity with how financial crime operations run day to day.
• Experience working with auditors or regulators, or supporting regulatory reviews and partner due diligence.

• Make a Global Impact:

Join a company that is transforming how people and businesses move money around the world. Your work will contribute to products and services that help millions of customers stay connected globally.

• Own Meaningful Outcomes:

We empower our people to take ownership, make decisions, and drive real impact. You'll have the opportunity to shape solutions, challenge the status quo, and see the results of your work firsthand.

• Grow with Us:

As a fast-growing global fintech, we offer opportunities to learn, develop new skills, take on new challenges, and advance your career across functions and geographies.

• Collaborate with Exceptional People:

Work alongside talented colleagues from diverse backgrounds and disciplines who are united by a shared ambition to build, innovate, and deliver great outcomes for our customers.

• Move Fast and Make Things Happen:

We value action, curiosity, and continuous improvement. You'll be part of a culture that encourages experimentation, embraces change, and focuses on delivering meaningful results.

• Be Part of Our Journey:

We're building the future of the global money movement. Join us at an exciting stage of growth where your ideas, expertise, and contributions can help shape what's next.