Risk Analyst

SHIELD is a device-first fraud intelligence platform that helps digital businesses worldwide eliminate fake accounts and stop all fraudulent activity. Powered by SHIELD AI, we identify the root of fraud with the global standard for device identification (SHIELD Device ID) and actionable fraud intelligence, empowering businesses to stay ahead of new and unknown fraud threats. We are trusted by global unicorns like inDrive, Alibaba, Swiggy, Meesho, TrueMoney, and more. With offices in LA, London, Jakarta, Bengaluru, Beijing, and Singapore, we are rapidly achieving our mission - eliminating unfairness to enable trust for the world.

Responsibilities

As a Risk Analyst, you will be involved in supporting risk operations by conducting data analysis of large datasets to discover fraud trends and patterns, and designing long-term solutions to fight fraud. Your insights will contribute to optimizing risk management strategies and creating business value by enabling trust for our clients. Responsibilities include:

• Handle end-to-end processes from data analysis, deriving insights, summarizing, and presenting recommended actions to stakeholders to stay ahead of new and unknown fraud.
• Monitor new or existing risk implementations and deployments, keeping up to date with the latest metrics of our clients and potential fraud trends.
• Provide timely response to alerts for risk-related escalations.
• Conduct deep-dive investigations on potential issues (e.g., changes to client metrics such as sudden dips or spikes); communicate and collaborate with relevant stakeholders to ensure timely resolution.
• Optimize fraud detection by rapidly identifying emerging fraud trends through data-driven analysis of complex, large datasets (e.g., device metadata, user data, payment data) and propose solutions to address them and prevent future occurrences.

Qualifications

• 1–3 years of hands-on analyst experience with risk knowledge.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or related fields.
• Experience handling large-scale unstructured data.
• Experience with business intelligence tools such as Microsoft Excel, Tableau, and Qlik Sense.
• Experience with SQL or other data handling tools is a plus.
• Ability to take initiative in fast-paced and dynamic environments and act promptly to prevent fraud risk.