At Finyard, we’re a global team of engineers, data scientists, marketeers, and financial experts, passionate about technology and innovation. We’re all about bringing revolutionary software services to people all around the world, and have been since 2018.
Our mission is to innovate by launching modern software solutions in the FinTech space, giving users around the world simpler and quicker ways to transact and manage their investments. We are committed to ensuring every product we release is in service of our users, so that as we grow, so do they.
Data Analyst (Payments) — Data Office
About the role
We are looking for a Data Analyst to join our Data Office and be allocated to the Payments domain. You will partner with Payments, Risk/Fraud, Product, and Engineering teams to monitor payment flows, detect and prevent abuse, improve payment performance, and build scalable analytics foundations (data marts, metrics, and alerting).
Key responsibilities
Monitoring & alerting
- Own and build day-to-day monitoring of payment flows and key risk indicators (deposits/withdrawals, approval rates, chargebacks, reversals, disputes, processor incidents).
- Build and maintain alerting for anomalies and suspicious patterns; define thresholds, escalation rules, and investigation playbooks.
Payments analytics & research
- Investigate user behaviour across web and mobile apps, focusing on payment journeys, drop-offs, and abnormal patterns.
- Conduct ad-hoc analyses and deep dives to explain deviations, incidents, and emerging risks.
- Run research to discover new fraud patterns, weak signals, and optimisation opportunities.
Anti-fraud rules & algorithms (payments)
- Develop and iterate anti-fraud rules, scoring approaches, and detection logic for payment abuse.
- Evaluate rule quality and impact (false positives/negatives, loss prevention, customer experience impact) and drive continuous improvement.
Tools for monitoring team
- Build dashboards and tooling for the monitoring/operations team (investigation views, drill-downs, case lists, segmentation views).
- Translate operational needs into clear requirements for Engineering/Data Engineering.
Segmentation & unit economics
- Create customer segmentation by payments risk (behavioural, transactional, device/network, processor outcomes).
- Build unit economics of a paying user (fees/costs, chargeback/dispute losses, fraud losses, net contribution, cohorts).
Experimentation, metrics & data marts
- Design and analyse A/B tests related to payment flows, risk controls, and UX changes.
- Formulate requirements and actively participate in building payments data marts and “single source of truth” datasets.
- Improve the payments/product metrics system: definitions, documentation, and consistency.
What we expect (must-have)
- 3+ years of experience in data analytics (fintech/payments/risk is a strong plus).
- Strong SQL+Python and hands-on experience working with large datasets.
- Experience with monitoring/alerting, anomaly investigation, and KPI ownership.
- Practical understanding of fraud/risk analytics (rule-based detection, segmentation, basic modelling).
Ability to communicate insights clearly and collaborate with cross-functional stakeholders.
Nice-to-have
- Experience with chargebacks/disputes, acquirers/processors, payment schemes, or AML-related analytics.
- A/B testing know-how (guardrails, bias, statistical pitfalls).
- Experience with product analytics tooling and event-based tracking.
Our technological stack
- Databases: Snowflake, ClickHouse, MySQL, BigQuery
- Visualisation systems: Tableau, HEX
- Analytics tools: Amplitude
- Programming language: Python (pandas, numpy, scikit-learn)
- Interactive environments: Jupyter Notebook, HEX
