Private Bank - Singapore - United Overseas Bank

    United Overseas Bank
    United Overseas Bank Singapore

    21 hours ago

    Full time $42,000 - $90,000 (SGD) per year *
    Description

    The Lead Data Scientist will architect and manage predictive analytics capabilities for the Private Banking division. The role focuses on transitioning from legacy, rule-based advisory logic to a robust, data-driven propensity modeling framework. The objective is to deploy explainable machine learning models that optimize investment product recommendations for high-net-worth clients while adhering to regulatory and governance standards.

    Key Responsibilities:

    Predictive Modeling & Analytics

    • Design, develop, and deploy production-grade machine learning models using Gradient Boosting frameworks such as XGBoost, LightGBM, and CatBoost.
    • Implement advanced sampling and resampling techniques (e.g., SMOTE, Class Weights) to address imbalanced datasets in Private Banking.
    • Architect complex financial feature sets derived from time-series transaction and client behavioral data.

    Explainability & Governance

    • Provide transparent, explainable recommendations using SHAP, LIME, and other model interpretability frameworks.
    • Ensure all models and analytics solutions comply with Model Risk Management (MRM) standards.
    • Maintain documentation, versioning, and reproducibility of models using tools like MLflow.

    Technical Architecture & Deployment

    • Collaborate with engineering teams to integrate models into production pipelines.
    • Implement workflow orchestration and scheduling using tools like Airflow.
    • Optimize SQL-based feature extraction and data pipelines for efficiency and scalability.

    Business Impact & Advisory Support

    • Work closely with Private Banking advisors and business stakeholders to translate model outputs into actionable investment insights.
    • Drive adoption of data-driven investment recommendations and support strategic decision-making.
    • Continuously improve modeling approaches based on performance metrics, regulatory feedback, and business priorities.

    Education & Experience

    • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Quantitative Finance, or related field.
    • 8–12 years of experience in developing and deploying ML/AI models in regulated financial environments.
    • Deep understanding of Wealth Management products, investment suitability, portfolio allocation, and rebalancing.

    Technical Skills

    Python 3.9+, Advanced SQL, Scikit-Learn, XGBoost, LightGBM, CatBoost, SHAP, LIME, Airflow, MLflow

    * This salary range is an estimation made by beBee
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