Data Scientist

Job Summary

We are looking for a skilled Data Scientist to design and implement a simulator that emulates financial activities associated with illicit behaviors such as money laundering, terrorist financing, fraud, and scams. Great opportunity to work on projects for a reputed bank.

Mandatory Skill-set

• Must have strong proficiency in Python programming, particularly in data simulation or synthetic data generation;
• Must have hands-on experience with PySpark for scalable data processing;
• Experience developing rule-based or typology-driven systems for data modeling;
• Must have solid understanding of financial crime scenarios such as money laundering, fraud, scams;
• Ability to work with limited labeled datasets and use statistical or machine learning methods for data inference;
• Experience designing and validating stress-testing scenarios in data simulation;

Desired Skill-set

• Background in data science, machine learning, or anomaly detection;
• Experience with financial services or compliance systems;
• Strong problem-solving and communication skills to collaborate across technical and non-technical teams.

Responsibilities

• Design and implement a simulator to mimic various financial crime behaviors and adversarial activities;
• Develop algorithms to generate synthetic customer profiles, accounts, and transaction data;
• Apply methods to learn from limited labeled data to enhance realism of simulated outputs;
• Incorporate predefined typologies and rule-based logic to simulate known patterns of financial crimes;
• Build functionality for stress-testing scenarios and edge-case behavior simulation;
• Generate transaction networks with interconnected counterparties to reflect realistic financial ecosystems;
• Collaborate closely with analytics and risk teams to align simulation logic with business requirements;
• Document technical designs and maintain codebase for reproducibility and scalability.

Should you be interested in this career opportunity, please send in your updated resume to ***** at the earliest.

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