Sr Fraud Analyst - US Remote

MoneyGram•New York, NY

3d•$75,000 - $135,000•Remote

About The Position

In this role, you will use your analytical and technical expertise to proactively identify fraud trends, design detection strategies, and influence data-driven decision-making across the organization. You'll partner closely with fraud operations, engineering, data science, and compliance teams to build scalable fraud analytics capabilities and enhance our defenses against evolving threats.

Requirements

Bachelor's or master's degree in a quantitative field (e.g., Data Science, Statistics, Economics, Computer Science, Mathematics).
3+ years of experience in fraud analytics, ideally in fintech, payments, or financial services.
Strong SQL skills and experience working with large-scale data sets (e.g., BigQuery , Snowflake).
Proficiency in Python or R for data analysis, statistical modeling, and automation.
Experience with BI tools (e.g., Looker, Tableau, Power BI) to create reports and dashboards.
Strong understanding of fraud patterns such as social engineering, synthetic identity, ATO, money mules, etc.
Ability to turn complex data into actionable insights and communicate findings clearly to both technical and non-technical stakeholders.

Responsibilities

Analyze large volumes of transactional, behavioral, and customer data to identify suspicious activity and emerging fraud patterns.
Design, develop, and maintain dashboards, KPIs, and visualizations to monitor fraud risk and operational performance.
Build and optimize fraud detection rules, scorecards, and machine learning features in collaboration with data scientists.
Perform root cause analysis on fraud cases and translate findings into actionable insights and system improvements.
Evaluate the effectiveness of existing fraud controls and recommend enhancements based on quantitative analysis.
Support A/B testing and impact assessments of new fraud tools, controls, or policy changes.
Assist with fraud loss forecasting, scenario modeling, and cost-benefit analysis for mitigation strategies.
Provide ad hoc analysis and insights to support leadership decisions and cross-functional initiatives.
Stay up to date on fraud tactics, tools, and industry benchmarks to ensure continuous innovation in detection strategies.