Senior Data Scientist

About the Role

Trust Bank Singapore is a digitally native bank built to create a better customer experience. The team works in a dynamic, collaborative setting where you will tackle new challenges, shape the future of the bank, and contribute to products that are useful, high quality, and customer-focused.

What You’ll Do

As a Senior Data Scientist, you will lead advanced analytics work that supports revenue growth and better customer outcomes. Your work will center on predictive modelling, machine learning, and business analytics, with a strong emphasis on turning data into practical actions for product and marketing teams.

• Build and deploy advanced machine learning models for cross-sell, upsell, deep-sell, and look-alike use cases to improve customer lifetime value and revenue per customer.
• Create customer segmentation, propensity scoring, next-best-action, and recommendation models to support personalised engagement.
• Set up automated model workflows from training to validation, deployment, and monitoring using AWS SageMaker Pipelines and modern MLOps practices.
• Use large language models such as Claude/Anthropic and AWS Bedrock to support insight generation, automated commentary, personalised content, and agent-style workflows.
• Turn model outputs into clear recommendations that can be used by product managers, marketing leaders, and senior stakeholders.
• Run A/B tests and champion-challenger experiments to evaluate business impact and measure key performance indicators.
• Work with data engineering teams to strengthen feature pipelines and maintain strong data quality for modelling inputs.
• Guide and mentor junior data scientists while promoting strong standards for model development, documentation, and reproducibility.

What You Need

You should bring a strong quantitative background and substantial hands-on experience in data science, especially in commercial or banking analytics. The role calls for deep expertise in statistical modelling, machine learning, and communicating insights to non-technical audiences.

• Master’s or PhD in Statistics, Mathematics, Computer Science, Economics, or another quantitative field.
• At least 7 years of practical data science experience, preferably in banking, financial services, or consumer platforms with a business analytics focus.
• Proven success in building and deploying production predictive models such as propensity, recommendation, segmentation, and lifetime value models.
• Strong command of statistical techniques including regression, classification, ensemble methods, Bayesian inference, and time-series analysis.
• Advanced Python skills with libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow, plus solid SQL capability.
• Practical experience with AWS SageMaker tools such as Training Jobs, Endpoints, Pipelines, and Feature Store for automation and deployment.
• Hands-on exposure to LLM and GenAI tools, including Claude and GPT, for prompt engineering, retrieval-augmented generation, and AI-assisted analytics workflows.
• Strong commercial judgment with the ability to connect patterns in data to revenue, cost, and customer experience outcomes.
• Excellent communication skills for presenting complex findings to business and technical stakeholders alike.

Preferred Experience

• Exposure to AWS Bedrock for building GenAI-powered applications and agents.
• Familiarity with causal inference and uplift modelling for campaign optimisation.
• Experience with retail banking products such as cards, loans, deposits, and wealth, as well as customer lifecycle analytics.
• Working knowledge of MLOps frameworks, CI/CD for machine learning, model monitoring, and drift detection.
• Experience designing real-time scoring systems and large-scale feature engineering.

Inclusive Workplace

Trust welcomes people as they are and values an open, inclusive, and respectful environment. Hiring decisions are based on business needs, role requirements, and individual qualifications. The organisation does not tolerate discrimination or harassment of any kind and encourages applicants of all ages and backgrounds.

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