Job Overview
- Job title: Credit Risk Modeling & Analytics Lead
- Direct Manager: CTO
- Working Location: Lottery Tower, 77 Tran Nhan Ton, An Dong Ward, HCMC
Established in 2019 as Vietnam's pioneering Buy Now, Pay Later provider, Fundiin is evolving into a data-first, AI-enabled platform for financial services in 2026. Fundiin partners with leading banks and financial institutions to orchestrate and optimize the distribution of a wide range of products - from BNPL and personal loans to credit cards - through a single and seamless app.
Leveraging proprietary data and AI-driven decisioning, Fundiin matches consumers with the most relevant offers, improving approval rates, user experience, and partner performance. By delivering a fully digital, end-to-end journey, Fundiin enables millions of users to access financial products with greater clarity, confidence, and control.
Role Overview
As a Credit Risk Analytics Lead at Fundiin, you will build and own our credit risk analytics capability - establishing the analytical processes, data foundations, and model development practices that power lending decisions across our platform.
The core of this role is analytical thinking: understanding data deeply, identifying the right problems to solve, and proposing solutions that fit - whether that is a model, a rule, a new data source, or simply better visibility for decision-makers. Models are one tool among many, not the goal in itself.
You will work closely with the Credit Policy & Portfolio team as their primary analytical partner, manage and develop the Risk Data Analyst, and build the modelling foundation the broader Data & AI group depends on. Specific use cases - scoring, fraud, propensity, pricing - will be prioritised over time. What matters most is building the capability and process discipline to do each one well.
Accountabilities
Data Analysis & Problem Framing
- Analyse credit, behavioural, and bureau data to surface risk signals, portfolio patterns, and anomalies
- Translate business questions into well-scoped analytical problems — and propose the right solution, whether a model, a rule, or a reporting framework
- Partner with Credit Policy & Portfolio as their primary analytical partner — turning data into outputs they can act on
Model Development Process & Methodology
- Establish and own the end-to-end model development process — from data collection and assessment, through feature selection, training, validation, and deployment
- Define and enforce data quality standards at each stage — ensuring every model is built on a reliable, well-understood data foundation
- Build reusable frameworks, tooling, and documentation that allow the process to scale across multiple use cases over time
- Own model monitoring and governance — tracking performance drift, maintaining audit trails, and ensuring models remain explainable in a regulated environment
Alternative Data & Feature Development
- Explore and evaluate new data sources — bureau, telco, e-commerce, behavioural signals — for predictive value
- Identify data gaps that limit risk assessment capability, and work with Data Engineering to address them
- Build and maintain a feature library that supports all risk and analytics use cases
- Work with Data Engineering to ensure features are available reliably at model serving time
Team Leadership
- Manage and develop the Risk Data Analyst - set priorities, review work, and provide technical guidance
- Define analytical standards and ways of working that scale as the team and use case coverage grows
Qualification & Experiences
Must Have
- 4–6+ years of experience in credit risk analytics, data science, or a quantitative role in financial services or fintech
- Strong analytical skills - able to read data, identify patterns and anomalies, and translate findings into actionable recommendations
- Experience establishing or improving a model development process - data collection, feature selection, validation, deployment - not just running existing pipelines
- Proficiency in Python (pandas, scikit-learn, XGBoost or equivalent) for analysis and model development
- Strong understanding of what makes good training data - and how to assess data quality before modelling
- Ability to work closely with business stakeholders - framing problems clearly, explaining analytical outputs, and proposing solutions that are both technically sound and business-relevant
- Strong ownership mindset - comfortable with ambiguity, drives clarity, and does not wait to be told what to do
Nice To Have
- Experience with bureau data integration and analysis - VCB, CIC, or equivalent
- Familiarity with fraud detection models or anomaly detection systems
- Experience with offer pricing models, income estimation, or lender matching logic
- Experience with A/B testing and experimentation frameworks for model changes
- Background in consumer lending, BNPL, or regulated financial environments
- Familiarity with explainability frameworks - SHAP, LIME, or equivalent
Benefits:
Why you love working with us:
At Fundiin, we believe in fostering a dynamic work environment that encourages personal and professional growth.
Empowerment and Growth:
- We offer great autonomy, freedom to make decisions, room to take risks and learn from mistakes.
- Get involved from the ground up in creating and developing new products, leading teams, working on innovative projects, and gaining visibility within the industry.
Competitive Benefits:
- Enjoy a robust benefits package, including meaningful ESOP options.
- Access our premium health care program and annual health checkup.
- Receive a budget of 2,400,000 VND per year for professional development.
- Take advantage of device allowances or financing options covering up to 50% of new device purchases.
Engaging Work Environment:
- Participate in our monthly Town Hall meetings and collaborative sharing sessions.
- 15 days of annual leave to recharge and pursue personal interests, plus an extra day for every 3 years of service.
- Join biannual team-building trips for bonding and connections.
- Enjoy free daily drinks and weekly TGIF snacks and beverages.
