Senior Data Scientist (Credit risk) - Fluent in English & Vietnamese

Role overiew: We're looking for a Senior Engineer to build the analytical and detection core of one of our key risk infrastructure products, serving financial institutions across Southeast Asia. You'll turn raw financial data into enriched features, credit risk signals, and fraud detection models and you'll ship those models into production yourself. This is a deeply technical, build-oriented role. You'll design and validate models, write the backend code that runs them at scale, and own the data pipelines behind them. You should think like a quant or credit modeler & build like a backend engineer.

• Working hours: Mon - Fri, 8:30am - 5:30pm - GMT+7 (Vietnam time)
• Location: Remote in Vietnam

I. REQUIREMENTS

• 5+ years in financial engineering, credit modeling, quantitative risk, or data science within lending/credit - with direct ownership of models that made real credit or fraud decisions.
• Fluent in English & Vietnamese, both verbal & written, equivalent to B2 CEFR / 6.5 IELTS (or similar) or higher - no English certification required.
• Strong credit modeling foundation: scorecards, PD/affordability models, credit risk underwriting logic, and model validation techniques.
• Backend coding ability: you can write clean, production-grade Python (or similar) and deploy models as services/APIs. Comfortable with software engineering practices: version control, testing, code review.
• Strong quantitative and statistical skills: feature engineering, model selection, performance evaluation, and statistical validation.
• Data engineering literacy: SQL fluency, experience building data pipelines, and working with large, messy real-world financial data.
• Experience with fraud detection (anomaly detection, rules engines, or ML-based fraud models) is strongly preferred.
• Ability to explain complex models clearly to product, business, and compliance stakeholders.

Nice to have:

• Experience with bank statement data, open banking, or financial document/transaction data.
• ML frameworks (scikit-learn, XGBoost, PyTorch/TensorFlow) and MLOps / model deployment tooling.
• Exposure to eKYC / eKYB / AML / RegTech domains and model governance requirements.
• Experience across Southeast Asian lending markets.

II. RESPONSIBILITY

• Design, build, and validate credit risk and fraud detection models - income estimation, cash flow and affordability analysis, transaction categorization, anomaly and fraud pattern detection.
• Engineer enrichment and feature pipelines that transform raw financial data into structured, decision-ready signals for underwriting.
• Write production backend code (Python and/or equivalent) to deploy models and enrichment logic as services and APIs - not just prototypes, but code that runs in production.
• Develop and tune fraud detection logic: rules engines, statistical anomaly detection, and ML-based approaches; define fraud typologies and translate them into detectable features.
• Own model performance end to end - define and monitor detection rates, false positive rates, precision/recall, PSI/stability, and coverage; retrain and recalibrate as data shifts.
• Build backtesting, validation, and monitoring frameworks to ensure model quality, explainability, and regulatory defensibility.
• Partner with engineering squads on data architecture, pipeline scalability, and deployment; partner with product and BD to align models with real underwriting workflows.
• Document methodology and model governance to a standard financial institutions and auditors can review.

Apply Now: Please carefully review the JD above. If the opportunity is a good fit for your skills & experience, kindly share your most up-to-date CV, clearly highlighting the relevant experience that aligns with the requirements.

You can submit your application either on Linkedin or on our company website: https://gct-solutions.net/career/recruitment/sr-data-scientist-credit-risk 🔗

Thank you for your interest!