Data Analyst (Risk)

SHIELD is the world’s leading risk intelligence company, empowering online businesses to stop fraud, build trust, and drive growth. Powered by the latest AI technology, SHIELD combines cutting-edge device fingerprinting with its proprietary Global Intelligence Network to detect new and unknown fraud threats in real time. SHIELD offers a range of solutions that span device fingerprinting, enterprise-grade protection, ad fraud prevention, and alternative credit risk intelligence. With offices across the globe and customers on every continent, SHIELD is rapidly achieving its global mission - to be the shield that enables trust for the world.

Responsibilities

As a Data Analyst (Risk), you will be involved in supporting the risk operations by conducting data analysis of risk cases to discover risk trends and behavior patterns from big 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 rich user and transaction data to uncover device, user, transaction, etc. 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
• Build and maintain dashboards for all stakeholders to provide visibility of key metrics, fraud patterns, and detection efficiency

Requirements

• 3-5 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
• Experience in SQL or other data handling tools, as well as the ability to learn more advanced data
• Business intelligence experience using tools such as Tableau, Qlik Sense, and Excel
• Working experience with any of the data analysis tools such as R, Python, SPSS, SAS, etc.
• 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