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Data Scientist (Vyntra)

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Job Description - Data Scientist (Vyntra)

Job Title: Data Scientist – AML (Python / Vyntra / NetGuardians)

Location: Singapore

Experience: 6–10 years

Job Description:

We are looking for a highly skilled Data Scientist with strong experience in Anti-Money Laundering (AML) systems and expertise in Python to join our banking analytics team. The ideal candidate will have hands-on experience with Vyntra (formerly NetGuardians) for transaction monitoring and anomaly detection.

Key Responsibilities:

  • Develop, enhance, and maintain data models and machine learning algorithms for AML detection and financial crime prevention.
  • Analyze large datasets from multiple sources to identify suspicious patterns and behavior.
  • Work closely with AML compliance, business, and technology teams to fine-tune Vyntra/NetGuardians models.
  • Implement and automate data pipelines and model deployment using Python-based frameworks.
  • Evaluate and improve model performance through feature engineering, tuning, and retraining.
  • Prepare analytical reports and dashboards for stakeholders highlighting insights and risk indicators.

Required Skills and Experience:

  • 6–10 years of experience in Data Science or AML analytics within the Banking/Financial Services domain.
  • Strong proficiency in Python, Pandas, NumPy, Scikit-learn, and data visualization tools.
  • Mandatory experience with Vyntra (formerly NetGuardians) platform for AML or fraud monitoring.
  • Deep understanding of AML processes, KYC, transaction monitoring, and financial crime compliance.
  • Experience working with large-scale structured and unstructured data sets.
  • Familiarity with SQL, ETL, and data warehouse concepts.
  • Excellent communication and stakeholder management skills.

Good to Have:

  • Exposure to cloud-based analytics platforms (Azure, AWS, or GCP).
  • Experience integrating AML systems with other data sources or case management tools.
  • Knowledge of model governance, explainability (XAI), and regulatory reporting standards.
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