Senior Data Scientist

Trust is the first of a new breed of banks in Singapore — digitally native and focused on delivering a delightful customer experience. As a Senior Data Scientist you'll solve some of the many interesting challenges we face and build delightful high-quality products for our customers.

Job Responsibilities

We are seeking a Senior Data Scientist with deep expertise in statistical modelling, machine learning, and business analytics to drive revenue growth through advanced analytical solutions. You will design and deploy predictive models including cross-sell, upsell, deep-sell, and look-alike models, leverage AWS SageMaker for end-to-end model automation, and harness Large Language Models (including Claude/Anthropic) to enhance productivity, personalisation, and insight generation at scale. You'll work with product owners, marketing, and business stakeholders to translate complex data patterns into actionable insights.

Key Responsibilities

• Design, build, and deploy advanced ML models for cross-sell, upsell, deep-sell, and look-alike use cases to maximise customer lifetime value and revenue per customer

• Develop customer segmentation, propensity scoring, next-best-action, and recommendation engines that inform personalised engagement

• Automate the end-to-end model lifecycle (training, validation, deployment, monitoring) using AWS SageMaker Pipelines and MLOps best practices

• Leverage LLMs (Claude/Anthropic) and AWS Bedrock for insight generation, automated commentary, personalised content, and agentic workflows

• Translate model outputs into clear, actionable business recommendations for product managers, marketing leads, and senior leadership

• Conduct rigorous A/B testing and champion-challenger frameworks to measure model impact on business KPIs

• Collaborate with data engineering teams to ensure robust feature pipelines and data quality

• Mentor junior data scientists and establish best practices for model development, documentation, and reproducibility

Requirements

• Master's or PhD in Statistics, Mathematics, Computer Science, Economics, or a quantitative discipline

• 7+ years of hands-on experience in data science with a strong focus on business/commercial analytics in banking, financial services, or consumer platforms

• Proven track record building and deploying production-grade predictive models (propensity, recommendation, segmentation, LTV)

• Deep expertise in statistical methods: regression, classification, ensemble methods, Bayesian inference, time-series analysis

• Strong proficiency in Python (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and SQL

• Hands-on experience with AWS SageMaker (Training Jobs, Endpoints, Pipelines, Feature Store)

• Experience with LLM/GenAI tools (Claude, GPT) for prompt engineering, RAG architectures, and AI-assisted analytics

• Exceptional business acumen connecting data patterns to revenue, cost, and customer experience outcomes

• Strong communication skills to present complex findings to non-technical stakeholders

Preferred Qualifications

• Experience with AWS Bedrock for building GenAI-powered applications and agents

• Familiarity with causal inference methods and uplift modelling for campaign optimisation

• Experience in retail banking products (cards, loans, deposits, wealth) and customer lifecycle analytics

• Knowledge of MLOps frameworks, CI/CD for ML, model monitoring, and drift detection

• Experience building real-time scoring systems and feature engineering at scale