1. Algorithm Model Development and Optimization: Design, develop, and deploy machine learning models and algorithms, optimizing and improving the algorithm models to enhance performance and accuracy;

2. Data Processing and Feature Engineering: Conduct data cleaning, preprocessing, and feature engineering to ensure data quality;

3. Algorithm Performance Evaluation: Design and execute experiments to assess model performance, using appropriate evaluation metrics and methods to validate model effectiveness;

4. Technical Research and Innovation: Stay updated with the latest research findings and technological trends in machine learning, proposing and implementing algorithmic solutions;

5. Cross-Departmental Collaboration: Work closely with team members to understand business requirements and translate them into technical implementations.