I’m looking for entry to mid-level Machine Learning or Data roles focused on data preparation, model evaluation, validation, and reliable ML pipelines for real-world products.
summer y
@summery
Machine Learning Engineer with experience in data annotation workflows, ML evaluation, and production-ready pipeline validation.
What I'm looking for
I work on practical, production-oriented machine learning and data workflows.
My experience includes:
- Data labeling, validation, and quality checks for ML datasets
- Building reproducible training and evaluation pipelines (Python, CLI-based workflows)
- Model evaluation with clear metrics, error analysis, and validation checks
- Supporting applied ML projects such as risk modeling and computer vision MVPs
I am comfortable with detailed guidelines, repetitive quality-focused tasks, and structured workflows, and I prioritize accuracy, consistency, and reliability in AI data and model evaluation work.
Experience
Work history, roles, and key accomplishments
Built production-style ML pipelines for credit risk and decision systems, focusing on reproducible training, time-aware validation, and model reliability.
Implemented baseline and tree-based models (Logistic Regression, LightGBM), integrated SHAP-based explainability, and enforced strict data validation to prevent leakage and unstable deployment behavior.
Developed an end-to-end object detection MVP with dataset integrity checks, simplified taxonomy, and deployment-oriented pipelines including ONNX export and INT8 CPU inference.
Focused on statistics, databases, and practical machine learning with emphasis on data validation, reproducibility, and explainable modeling.
Education
Degrees, certifications, and relevant coursework
xi'an peihua university
Bachelor's Degree, Computer Science and Technology
2018 - 2022
Grade: 3.5 / 4.0
Activities and societies: Self-directed technical projects in data analysis and machine learning emphasizing practical modeling, system reliability, and deployment-aware practices.
Completed a Bachelor's degree focused on statistics, databases, and practical machine learning with emphasis on data validation, reproducibility, and model explainability.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Salary expectations
Social media
Job categories
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