Data Analyst

Description

We are looking for a Data Analyst to support fraud-focused data initiatives in Pittsburgh, Pennsylvania. This Long-term Contract position is ideal for someone who can turn complex information into clear insights that help identify suspicious activity and strengthen anti-fraud efforts. The role combines analytical problem-solving with investigative support, requiring close attention to patterns, anomalies, and data quality.

Responsibilities

• Analyze large datasets to detect unusual trends, irregular transactions, and indicators of potentially fraudulent activity.
• Develop reports and dashboards that translate fraud-related data into actionable findings for business and risk stakeholders.
• Partner with investigation and compliance teams to support case reviews with accurate data analysis and documented insights.
• Examine data sources for consistency and reliability to ensure fraud monitoring outputs are based on high-quality information.
• Identify emerging patterns and recommend adjustments to analytical approaches that improve fraud detection effectiveness.
• Perform ad hoc analysis to answer urgent business questions related to anti-fraud controls, losses, and suspicious behavior.
• Maintain clear records of analytical methods, assumptions, and findings to support auditability and ongoing investigations.

Requirements

• Experience working in data analysis roles with a focus on fraud, risk, or investigative support.
• Strong understanding of suspected fraud indicators, fraud analytics methods, and anti-fraud practices.
• Ability to interpret complex datasets and uncover meaningful patterns, anomalies, and trends.
• Background supporting fraud investigation efforts through reporting, analysis, and evidence-based insights.
• Proficiency in organizing and validating data to produce accurate and dependable analytical results.
• Strong critical thinking and problem-solving skills with a high level of attention to detail.
• Effective communication skills with the ability to present findings clearly to cross-functional teams.

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