About Wise
Wise is a global technology company building a way to move and manage money worldwide. It helps people and businesses send money internationally, spend abroad, and make and receive international payments, with the aim of making these activities easier and less expensive.
Responsibilities
•Proactively identify anomalies, blind spots, and gaps in fraud controls using transaction, event, and case data
•Build rules and features for the detection engine to catch fraudsters
•Deliver findings and recommended actions that lead to operational fixes
•Analyze root causes and cohorts to diagnose control performance and propose prioritized remediation
•Assess customer experience affected by controls and drive continuous improvements
•Define and own fraud KPIs across products and markets with Product Managers, Data Scientists, Operations, and Analysts
•Identify, prioritize, and drive initiatives with Product Managers
•Link business outcomes to control-level metrics and own team performance
•Serve as the primary analytical contact for fraud workstreams on new market launches
•Model pre-launch risk scenarios to estimate exposure and operational load
•Provide go/no-go recommendations balancing risk, cost, and customer impact
•Choose high-impact problems, create a vision, and own execution
•Partner cross-functionally with product managers, engineers, analysts, investigators, compliance, and operations across markets
•Inform Product and Risk Committees and shape strategic decisions through analysis
•Deliver measurable change that protects customers and enables growth
Requirements
•At least 3 years of product analytics experience in a complex, data-driven environment
•Ability to take work beyond analysis and deliver results
•Strong communication skills, including translating complex analyses into clear recommendations and influencing senior stakeholders
•Strong analytical thinking and product intuition focused on payments, transaction flows, and online behavior
•Ability to investigate data, identify anomalies, communicate findings, and drive action across teams
•Advanced SQL and Python (pandas/scikit or similar)
•Experience building dashboards in BI tools such as Looker, Tableau, or PowerBI
Nice to have
•FinTech/payments and fraud experience
•Feature engineering and model monitoring
•Familiarity with Airflow/DBT/Git/Snowflake and production data platforms
Conditions
No conditions specified in the posting.
