SHIELD is a device-first risk AI platform that helps digital businesses worldwide eliminate fake accounts and stop all fraudulent activities. SHIELD identifies the root of fraud with the global standard for device identification (SHIELD Device ID) and actionable risk 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 San Francisco, Miami, London, Berlin, Jakarta, Bengaluru, Beijing, and Singapore, we are rapidly achieving our mission – eliminating unfairness to enable trust for the world.
Responsibilities
As a Data Analyst (Risk), you will be involved in supporting the risk operations by retrieving meaningful and relevant information from large databases, conducting data analysis to discover fraud trends and patterns. Thereafter, helping in designing long-term solutions to fight fraud. The insights you provide will contribute towards optimizing risk management strategies and creating business value by enabling trust for our clients. Responsibilities include:
- Handle end to end process from retrieving data, data analysis, deriving insights, summarizing, and presenting recommended actions to stakeholders in order for us to stay ahead of new and unknown fraud.
- Utilize statistical tools to identify areas of potential fraud risk and/or potential areas of improvements to current fraud detection mechanisms.
- Collect, validate, and clean data from various sources to ensure data accuracy and reliability.
- Optimize fraud detection by rapidly identifying emerging fraud trends through data-driven analysis in complex and large dataset such as device metadata, user data, payment data – and coming up with proposals to address them and preventing future occurrence.
- Improve and maintain dashboards to provide visibility of key metrics and fraud patterns for relevant stakeholders.
Requirements
- 2 – 5 years of experience as a hands-on data analyst.
- Minimum Bachelor’s Degree in Computer Science, Data Sciences, Statistics, Mathematics, or other related fields.
- Working experience in handling large-scale unstructured data.
- Proficiency in SQL or other data handling tools.
- Experience in using business intelligence tools such as Tableau, Qlik Sense, and Microsoft Excel.
- Experience with any data analysis tools such as Python, R, SPSS and SAS.
- Ability to take initiative in a fast-moving and dynamic environments, and take timely actions to prevent risk of fraud.
