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Summary

We are hiring a hands-on Lead to build and own a company-wide monitoring and anomaly detection function. You will define the most important business and risk metrics, implement continuous monitoring and alerting, and drive fast detection of abnormal behavior (including fraud signals and issues impacting revenue, customer experience, and operations). You will personally design metrics, build dashboards, implement alert logic, investigate anomalies, and establish playbooks, while coordinating cross-functional response with Product, Risk/Fraud, Operations, Data and Engineering.

Key Responsibilities

• Define monitoring strategy and KPI system: identify critical business and risk metrics across the funnel (acquisition, activation, retention, payments, credit/loans, support, etc.).
• Build anomaly detection and alerting: set thresholds, baselines, seasonality-aware rules, and anomaly models where appropriate; reduce false positives/negatives over time.
• Anti-fraud and risk monitoring: design and maintain dashboards and alerts for fraud patterns, suspicious user behavior, chargebacks/disputes, account takeover indicators, synthetic identity signals, and other abuse vectors.
• Operational and reliability monitoring (as needed): partner with engineering to incorporate key technical signals that directly impact business.
• Create monitoring playbooks: define severity levels, investigation steps, escalation paths, and ownership (who acts, how quickly, and what “done” means).
• Root cause and post-incident analysis: conduct investigations, quantify impact, and implement prevention/early-warning improvements.
• Stakeholder alignment: run regular reviews with Product/Risk/Ops/Engineering, iterate on metrics, and prioritize the monitoring backlog.
• Documentation & governance: maintain a “single source of truth” for definitions, formulas, and alert logic.

Required Qualifications

• 3+ years in data analytics, risk/fraud analytics, product analytics, business intelligence, or monitoring/observability roles.
• Strong ability to translate business processes into measurable metrics and signals.
• Hands-on experience with dashboards and reporting (Power BI / Grafana / Azure App Insights).
• Strong SQL; comfortable working with large datasets and building repeatable metric pipelines.
• Understanding of anomaly detection.
• Strong communication skills: you can explain “what happened, why it matters, what we do next” to both technical and non-technical teams.

Nice to Have

• Experience in fintech/payments/lending and fraud/abuse prevention.
• Experience with event-based architectures and monitoring operational signals (queues, webhooks, vendor APIs).
• Familiarity with experimentation metrics, funnel analytics, cohort analysis.
• Python skills for analytics, detection logic, and automation.