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Associate, Risk Data Scientist

location

Kuala Lumpur, Malaysia

permanent

Permanent

Duties & Responsibilities

  • A Data Scientist in Fraud Risk typically has responsibilities focused on identifying, analyzing, and mitigating fraud risks for TNGD.
  • Build Models and Algorithms:
  • Develop and implement machine learning models and algorithms to detect and prevent fraudulent activities.
  • Design rules and frameworks to identify and mitigate fraud and credit risks across various products within TNGD platforms.
  • Data Analysis and Risk Assessment:
  • Analyze large datasets to uncover patterns, trends, and anomalies that may indicate fraudulent activity.
  • Utilize statistical methods to assess the risk and impact of potential fraud on the business.
  • Process Improvement:
  • Develop and refine processes to root out high-risk activities, ensuring that fraud detection methods are efficient and effective.
  • Collaboration:
  • Work closely with cross-functional teams, including engineering, product management, and BI team, to integrate fraud detection tools and strategies.
  • Innovation and Learning:
  • Stay updated with the latest trends and advancements in fraud detection and machine learning.
  • Seek opportunities to innovate and enhance existing fraud prevention techniques.
  • Reporting and Communication:
  • Communicate findings and insights to stakeholders through reports and presentations.
  • Make recommendations based on data-driven insights to influence strategic decisions.

Requirements

  • Education:
  • A bachelor's degree or higher in computer science, data science, machine learning, or a related technical field.
  • Experience:
  • A minimum of 1 year of experience in data analysis, modeling, and visualization.
  • Technical Skills:
  • Proficiency in programming languages such as Python and SQL.
  • Experience with data visualization tools like Looker.
  • Strong skills in machine learning and algorithm development for fraud detection.
  • Analytical Skills:
  • Ability to analyze large datasets to identify patterns, trends, and anomalies indicative of fraud.
  • Soft Skills:
  • Strong communication skills to effectively present findings and insights to stakeholders.
  • An act-like-an-owner mentality, showing initiative and responsibility for tasks.
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