About the job

Company Description

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Role Description

This is a full-time on-site role for a Senior (Fraud Data) Cybersecurity Analyst at Blackbox Technologies Pvt Ltd located in Kathmandu. The Senior (Fraud Data) Cybersecurity Analyst will be responsible for analyzing and monitoring fraud data to identify potential security breaches and developing strategies to prevent future incidents. They will also collaborate with cross-functional teams to implement effective security measures and provide recommendations for enhancing cybersecurity protocols.

Requirements:

• Bachelor’s degree from exact-sciences-related areas such as Statistics, Economics, Mathematics, Industrial engineering, Computer Science, or a related field.
• Master's degree and or an active student for a Master’s degree - worked on, or actively working on a thesis - an advantage
• At least 6 years of experience as a data analyst.
• Experience in fraud/risk analysis in payment fraud.
• Experience in one of the following domains; Gaming, Gift cards, or Crypto- an advantage 
• Significant experience in applying statistical techniques.
• Strong proficiency in working with SQL databases.
• Strong proficiency in Python and the Pandas package.
• Understanding of general concepts in the field of Machine Learning - an advantage.
• Excellent expression skills both verbal and in writing in English are a must.
• Operative and creative thinking.
• Ability to work in an operational, dynamic, multitasking environment.
• Team player.
• Someone who loves the startup spirit and would like to join the ride.

Responsibilities:

• Track fraud trends by analyzing accounts and transaction patterns.
• Analyze deep transactional and behavioral data patterns and trends and provide data insights.
• Research reasons for deviation from baseline, and collaborate with data science teams for taking corrective measures.
• Identify and foreseen future fraud trends and patterns. 
• Observe customer transactions to identify fraudulent activity.
• Design and develop analytics infrastructure for researching scalable fraud.