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.
