Senior Data Scientist at Trust Bank

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

30 Sep, 26

Salary

0.0

Posted On

02 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Statistical Modelling, Machine Learning, Business Analytics, AWS SageMaker, Large Language Models, Python, SQL, MLOps, A/B Testing, Customer Segmentation, Propensity Scoring, RAG Architectures, Prompt Engineering, AWS Bedrock, Predictive Modelling, Causal Inference

Industry

Financial Services

Description

Trust is the first of a new breed of banks in Singapore – digitally native and focused on delivering a delightful customer experience. You will work in a fast-paced and collaborative environment to solve new and interesting challenges each day. Together with our Trust team, you will help shape the future of our bank. As a Senior Data Scientist you'd be able to work on and solve some of the many interesting challenges we are facing, learn new ways of working, 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. In this role, you will design and deploy predictive models including cross-sell, upsell, deep-sell, and look-alike models that directly impact product strategy and customer engagement. You will leverage AWS SageMaker for end-to-end model automation and harness Large Language Models (LLMs) including Claude/Anthropic to enhance productivity, personalisation, and insight generation at scale. You will work closely with product owners, marketing, and business stakeholders to translate complex data patterns into actionable insights at both product and customer levels, ensuring analytical solutions drive measurable business outcomes. 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 strategies. Automate 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 creation, and agentic workflows that augment human decision-making. 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 for model inputs. Mentor junior data scientists and establish best practices for model development, documentation, and reproducibility. In order to be successful at this role, you must have the following: 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) for model automation and deployment. Experience with LLM/GenAI tools (Claude, GPT) for prompt engineering, RAG architectures, and AI-assisted analytics workflows. Exceptional business acumen - ability to connect 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. Role Specific Technical Competencies Strong proficiency in Python (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and SQL. Hands-on experience with AWS SageMaker (Training Jobs, Endpoints, Pipelines, Feature Store) for model automation and deployment. Experience with LLM/GenAI tools (Claude, GPT) for prompt engineering, RAG architectures, and AI-assisted analytics workflows. Experience with AWS Bedrock for building GenAI-powered applications and agents. Knowledge of MLOps frameworks, CI/CD for ML, model monitoring, and drift detection. If you apply for a job with Trust or submit any personal information in connection with a possible job opportunity, you agree to our privacy notice for job applicants. Come as you are! Trust is an inclusive and open-minded workplace. If you are good at what you do and care about doing a good job, that’s what we focus and want from you. So come as you are. 😊 Trust is an equal opportunity employer. We prohibit discrimination and harassment of any kind. We are committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. All employment decisions at Trust are based on business needs, job requirements and individual qualifications, without regard to age, gender, physical ability, race, religion or belief, family or parental status, sexuality, or any other status protected by laws or regulations. We will not tolerate discrimination or harassment based on any of these characteristics. We encourage applicants of all ages.

Responsibilities

Design and deploy advanced predictive models for cross-sell, upsell, and customer segmentation to drive revenue growth. Leverage LLMs and AWS SageMaker to automate model lifecycles and generate actionable business insights.