Senior Data Scientist

About Trust Bank Singapore

Trust Bank is a pioneering digitally-native bank in Singapore, committed to delivering outstanding and seamless customer experiences. Working here means engaging with dynamic challenges in a collaborative setting while contributing to the innovative direction of our financial products and services.

Key Responsibilities

• Create, implement, and manage sophisticated machine learning models focusing on cross-selling, upselling, deep-selling, and look-alike targeting to optimize customer lifetime value and increase revenue.
• Develop customer segmentation schemes, propensity scores, next-best-action recommendations, and engines that support tailored customer engagement tactics.
• Automate the complete lifecycle of machine learning models — from training and validation to deployment and monitoring — by utilizing AWS SageMaker Pipelines alongside modern MLOps standards.
• Apply Large Language Models such as Claude and Anthropic, as well as AWS Bedrock services, to generate insights, automate content generation, and enhance decision-making with AI-assisted workflows.
• Communicate complex model results in clear and actionable terms to product teams, marketing departments, and senior executives to drive informed business decisions.
• Lead controlled experiments including A/B testing and champion-challenger setups to gauge the business impact of deployed models.
• Collaborate closely with data engineering teams to ensure accurate and efficient data pipelines and data quality for modeling inputs.
• Provide mentorship and guidance to junior data scientists, fostering best practices in model development, documentation, and reproducibility.

Required Qualifications and Experience

• Master’s or PhD degree in Statistics, Mathematics, Computer Science, Economics, or a similar quantitative field.
• At least seven years of practical experience in data science focused on business analytics within banking, financial services, or consumer platforms.
• Demonstrated ability to create and deploy production-ready predictive models such as for propensity, recommendations, segmentation, and customer lifetime value.
• Advanced knowledge of statistical approaches including regression, classification, ensemble methods, Bayesian techniques, and time series analysis.
• Expertise in programming with Python and SQL, with familiarity using Python libraries like scikit-learn, XGBoost, LightGBM, and deep learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience managing full-fledged model automation and deployment through AWS SageMaker (including training jobs, endpoints, pipelines, and feature store).
• Practical use of Large Language Models and Generative AI technologies (such as Claude and GPT) for engineering prompts, retrieval-augmented generation, and AI-supported analytics workflows.
• Strong business insight to link analytical patterns to revenue, cost savings, and customer satisfaction improvements.
• Excellent communication skills for translating technical results to non-technical audiences.

Preferred Additional Skills

• Experience working with AWS Bedrock to develop GenAI-enhanced applications and intelligent agents.
• Knowledge of causal inference techniques and uplift modeling for marketing campaign optimization.
• Familiarity with retail banking products and customer lifecycle management analytics.
• Expertise in MLOps practices including continuous integration and delivery (CI/CD) for machine learning, model monitoring, and detecting model drift.
• Background in creating real-time scoring systems and handling large-scale feature engineering.

Culture and Diversity

Trust fosters an inclusive and respectful workplace, valuing diverse backgrounds and perspectives. We emphasize quality work and commitment over any personal characteristic. Employment decisions are made based on qualifications and business needs without discrimination of any kind, including age, gender, race, religion, physical ability, or parental status, among others.