Job Responsibilities
Utilize statistical analysis, machine learning modeling, and other methods to deeply explore the business value of multi-source heterogeneous data, building predictive, decision-making, and optimization models for business scenarios.
Manage the full lifecycle of AI models, including requirement analysis, data preparation, feature engineering, model training, evaluation, deployment, and monitoring, ensuring continuous model iteration and optimization.
Design and implement model evaluation systems to continuously optimize model predictive performance, stability, interpretability, and business alignment, ensuring technical solutions closely align with business objectives.
Be responsible for the full process of data modeling, including data cleaning, feature engineering, model building, evaluation, optimization, and business implementation.
Collaborate closely with product, Development, business, and other teams to jointly promote the implementation, effectiveness evaluation, and iterative enhancement of AI solutions.
Keep abreast of cutting-edge technologies in machine learning and artificial intelligence (e.g., large language models RL, Transformers, Explainable AI) and explore their innovative applications in business scenarios.
Job Requirements
Strong proficiency in programming languages in Python/R/SQL, with familiarity in mainstream machine learning frameworks like TensorFlow/PyTorch.
Hands-on experience in the full data modeling lifecycle, understanding principles of common algorithms, and awareness of large language model technology trends.
Deep understanding of classic machine learning algorithm principles and applicable scenarios (LR, RF, XGBoost/LightGBM, SVM, K-Means, Collaborative Filtering, Neural Networks, etc.), mastery of model evaluation metrics (Accuracy, Recall, AUC, MAE, RMSE, NDCG, etc.) and optimization techniques (Cross-Validation, Grid Search, Feature Selection); familiarity with deep learning models (e.g., CNN, LSTM, Transformer) and possessing practical deep learning model experience.
Hands-on experience and provable case in supply chain models, such as demand planning, APS, logistic network and etc.
Familiarity with tools such as Hadoop/Spark/Flink, with extensive data development experience, and a deep understanding and practical expertise in data processing, data modeling, and data analysis.
Strong logical thinking and cross-team communication skills, capable of effectively coordinating technical resources to drive project implementation.
Commercial data sensitivity, able to identify business growth opportunities through data insights, with innovative awareness and implementation capability.
Bachelor’s/Master’s degree in Computer Science, AI, or a related field.
3+ years of algorithm and model development experience, modeling experience in electronic or industrial
Having led enterprise-level machine learning platform construction or core module refactoring, with experience in algorithm implementation in complex business scenarios (including performance tuning for training and inference) in a plus.
Familiarity with supply chain business is a plus
