Intern (Data Science)

Job Summary

As an Intern Analyst within the Fraud Strategies team, you will work on high-impact initiatives that directly shape the company’s fraud mitigation and risk management strategies. In this role, you will leverage large-scale datasets to identify emerging fraud patterns and support the creation of data-driven decision-making across the organization. This position is designed for an individual pursuing a bachelor’s degree in a quantitative field to use analytical techniques to leverage solve real-world fraud and risk challenges at scale.

You will work cross-functionally with product managers, data scientists, data engineers, and technical teams to evaluate new and existing datasets, surface actionable insights, and enhance fraud controls across platforms.

Responsibilities:

• Analyze millions of transaction records to identify fraud trends and risk signals using SQL
• Identify and evaluate new signals from device, behavioral, and geographic data to strengthen fraud prevention strategies
• Perform in-depth fraud analytics to come up with rule opportunities that
• Document analytical findings and communicate insights and recommendations to stakeholders and leadership
• Conduct exploratory data analysis to identify anomalies, key drivers, and significant features that inform fraud risk management decisions

Qualifications:

• Working towards a bachelor’s degree in data science, Computer Science, Statistics, Engineering, or a related quantitative field
• Working knowledge of SQL, including the ability to write basic queries for data validation and investigation.
• Proficiency in SQL for querying and analyzing large datasets
• Proficiency in Microsoft Excel, including formulas, filtering, pivot tables, and basic data analysis.
• Experience with analytics and reporting tools (e.g., Python and Power BI) is a bonus.
• Demonstrated interest or experience in fraud, cybercrime, payments, or financial risk modeling
• Strong written and verbal communication skills with the ability to present complex analyses clearly
• Self-motivated, detail-oriented, and able to prioritize work effectively in a fast-paced, collaborative environment