About the role
We are looking for a Data Science Expert to join our department and turn the bank's data into commercial outcomes - more relevant offers, higher customer lifetime value and sharper business planning.
This is a business-facing role, not a risk modelling role. Credit, market and operational risk models are owned by a separate function. Your models will be judged by the revenue they generate, the campaigns they lift and the decisions they change - measured in real business results.
You will work end-to-end: framing the business problem, building the model, deploying it into production, and proving the incremental impact.
Key Responsibilities
Customer intelligence & lifetime value
Build and productionise customer-level models: segmentation, propensity to buy, next-best-offer/next-best-action, churn and attrition, and Customer Lifetime Value across the full lifecycle (acquisition → onboarding → cross-sell → retention → win-back).
Identify white-space opportunities in cross-sell and up-sell across deposits, cards, loans, bancassurance and wealth products, and translate them into sized, prioritised commercial opportunities.
Deepen the customer view by combining transaction behaviour, channel/digital journey data, product holdings and demographics.
Marketing & personalisation analytics
Power personalised campaigns across digital app, web, contact centre and branch channels with real-time and batch model scores.
Design and evaluate customer-level A/B tests and control-group experiments; establish causal, incremental measurement of campaign lift, response rate and cost per acquisition.
Partner with Marketing to move budget allocation from intuition to evidence, and to build always-on trigger-based journeys instead of one-off blasts.
Forecasting, pricing & business planning
Build forecasting models for business volume, balance growth, product take-up, fee income and channel demand to support annual planning and monthly business reviews.
Support pricing and offer strategy with elasticity analysis, scenario simulation and profitability modelling at customer and product level.
Deliver executive-quality insight to business heads and senior management: clear, quantified, decision-ready.
Delivery & standards
Own the full model lifecycle: problem framing, data exploration, feature engineering, development, validation, deployment, monitoring and recalibration.
Work with data engineers and the platform team to industrialise models on the bank's on-premise data and AI infrastructure.
Contribute reusable data assets, features and code to the team, and coach analysts on analytical rigour.
Apply the bank's data governance, privacy and model documentation standards throughout.
Requirements
Must have
5-15 years of hands-on data science experience, with a meaningful portion in banking, fintech, insurance, telco, e-commerce or another customer-data-rich industry.
Advanced SQL and strong Python (pandas, scikit-learn); comfortable working with large, messy production data.
Proven track record of models that reached production and generated measurable business value - you can quantify the lift you delivered.
Solid grounding in statistics and experiment design: hypothesis testing, sampling, control groups, causal inference basics.
Strong commercial instinct: you start from the P&L question, not from the algorithm.
Ability to explain complex analysis to non-technical executives in plain business language, in Vietnamese and English.
Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering or a related quantitative field.
Nice to have
Experience with gradient boosting frameworks, uplift modelling, recommendation systems or survival analysis.
Familiarity with Oracle/enterprise data warehouses, Spark, Airflow, MLflow, Docker or MLOps practices.
Experience with marketing automation, CDP or campaign management platforms.
Exposure to LLM/GenAI applications in a business analytics context.
Understanding of banking products, core banking data structures and Vietnamese banking regulations.
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