Senior Financial Crime Data Scientist
3 days ago
Job Opportunity
The Challenge: Delivering Insights in Financial Crime Data Science
- We are seeking a seasoned professional to lead a project focused on developing a simulator for financial crime data in Singapore.
Key Responsibilities:
- Design and develop an advanced simulator that accurately mimics financial activities of various adversaries, including money laundering, terrorist financing, money mule, fraud, and scam.
- Develop and implement cutting-edge algorithms to generate synthetic data, including mock customer profiles, accounts, and transaction histories.
- Incorporate approaches to learn from limited existing labelled data to inform synthetic data generation, driving accuracy and relevance.
- Implement functionality to generate data based on predefined financial crime typologies, rules, and stress-testing scenarios, ensuring comprehensive coverage.
- Ensure the simulated data captures suspicious patterns through interconnected counterparty information, reflecting realistic network behaviors, providing actionable insights.
- Work within a Python-based development environment, utilizing PySpark for data processing tasks, leveraging expertise in data science and analytics.
- Collaborate with the financial crime analytics team to understand requirements and refine simulation logic, fostering a culture of collaboration and excellence.
Requirements and Skillsets:
Technical Skills:
Python (4–6 years)
Machine Learning (4–6 years)
Apache Spark / PySpark (1–2 years)
Statistical Modeling (2–4 years)
Analytical Expertise:
Statistical Analysis (2–4 years)
Additional Skills:
Strong analytical and problem-solving capabilities, ability to work independently and meet deadlines, excellent communication and collaboration skills.
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