Senior Machine Learning Engineer
On-siteShenzhen Shenzhen, Guangdong, China
Job Summary
Design, develop, and productionize ML models for credit scoring, underwriting, fraud detection, collections, and portfolio risk management using algorithms like XGBoost. Build robust features from customer, transaction, repayment, behavioral, and alternative data while addressing class imbalance, data leakage, bias, and model stability. Define offline and online evaluation metrics aligned with lending outcomes and establish reliable training, validation, deployment, monitoring, and retraining pipelines. Monitor model performance, calibration, drift, fairness, and operational impact in production. Collaborate with data and software engineers to integrate models into scalable systems, produce clear documentation for stakeholders, and explore LLM applications for document processing and underwriting assistance. Requires 5+ years of ML engineering experience in fintech, digital lending, or microfinance.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or a related field, or equivalent practical experience
- 5+ years of ML Engineering or Data Scientist experience
- Strong foundation in traditional machine learning, including classification, regression, feature engineering, model evaluation, and imbalanced-data handling
- Hands-on experience with models such as XGBoost, LightGBM, random forests, and logistic regression
- Proficiency in Python, SQL, and ML libraries such as scikit-learn, XGBoost, pandas, and NumPy
- Experience deploying, monitoring, and maintaining ML models in production
- Understanding of model explainability, drift detection, experiment tracking, and reproducible ML workflows
- Knowledge of credit risk, underwriting, fraud detection, customer scoring, or related financial-services use cases
Desired Qualifications
- Experience in fintech, digital lending, or microfinance
- Familiarity with cloud platforms, containers, APIs, and data pipelines
- Exposure to LLMs, retrieval-augmented generation, prompt engineering, or agentic programming
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