Data Analyst (Risk)

SHIELD; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading fraud intelligence platform is seeking a Data Analyst (Risk) to support risk operations by analyzing data to uncover fraud trends and patterns. The role involves optimizing fraud detection strategies and providing insights to stakeholders. Candidates should have experience with large datasets and proficiency in data handling tools.

Qualifications

• 2 – 5 years of experience as a hands-on data analyst.
• Working experience in handling large-scale unstructured data.

Responsibilities

• Handle end to end process from retrieving data to presenting recommended actions.
• Utilize statistical tools to identify areas of potential fraud risk.
• Optimize fraud detection by identifying emerging fraud trends.

Skills

Data Analysis

Statistical Tools

SQL

Initiative

Education

Bachelor’s Degree in Computer Science

Bachelor’s Degree in Data Sciences

Bachelor’s Degree in Statistics

Bachelor’s Degree in Mathematics

Tools

Tableau

Qlik Sense

Microsoft Excel

Python

R

SPSS

SAS

Job description

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 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.