BlackBox Technologies

Senior Fraud Data Analyst (IL)

BlackBox Technologies

Senior Fraud Data Analyst (IL)

Senior Fraud Data Analyst (IL)

BlackBox Technologies

Kathmandu
Experience: More than 4 years
Key Skills: Data Science Databases Pandas (Software) Extract Load (Etl)

Senior Fraud Data Analyst (IL)

Views: 250 | Apply Before: 1 week, 6 days from now

Basic Job Information

Job Category : Others
Job Level : Mid Level
No. of Vacancy/s : [ 1 ]
Employment Type : Full Time
Job Location : Kathmandu
Apply Before(Deadline) : Jul. 13, 2024 23:30 (1 week, 6 days from now)

Job Specification

Education Level : Under Graduate (Bachelor)
Experience Required : More than 4 years
Professional Skill Required : Data Science Databases Pandas (Software) Extract Load (Etl)

About the job

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 4 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.

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