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