SHIELD is a device-first risk intelligence company. We are dedicated to helping organizations worldwide eliminate fake accounts and all malicious activity. Leveraging AI, we identify the root of fraud with the SHIELD Device ID and provide actionable risk intelligence in real time, helping all online businesses stop fraud, build trust, and drive growth. 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 Risk Analyst, you will be involved in supporting the risk operations by conducting data analysis of risk cases to discover risk trends and behaviour patterns from data sets, thereafter being involved in designing long-term solutions to fight fraud. The insights you provide will be optimizing risk strategies to create business value and enable trust for our clients.
- Analysis of user and transaction data to uncover patterns including device, user and transaction trends that help alert SHIELD's risk systems and contribute to fraud prevention mechanisms.
- Optimize fraud detection by rapidly identifying emerging fraud trends through data-driven analysis and developing strategic fraud rules to address them.
- Perform data/statistical analysis to keep processes at the forefront of fraud detection by identifying areas of potential fraud risk and/or potential opportunities to improve current fraud mechanisms.
- Develop and communicate insights and recommended actions to stakeholders to manage risk by contributing toward machine learning models, and risk management principles to help clients trust their users by staying ahead of new and unknown fraud.
- 1-3 years of experience as a hands-on analyst in a high-tech company
- Minimum Bachelor Degree in Computer Science, Data Sciences, Statistics, Math, or other related fields
- Strong experience in handling large-scale unstructured data
- Proficiency in programming language and data analysis tools such as SQL or Python
- Business intelligence experience using tools such as Microsoft Excel
- Experience with the application of experimentation and statistical techniques (i.e. hypothesis testing, probability distributions, regression, decision trees, etc.)
- Ability to take initiative in a fast-moving and dynamic environment, and take timely actions to prevent risk of fraud
