Data Analyst at MEXC

Job Description

Key Responsibilities

• Collect, clean, and annotate customer service data, including user conversations, question types, and answer content, for use in training machine learning models. This involves organizing raw data into structured formats suitable for algorithmic processing.
• Monitor model training processes to ensure optimal performance on customer service data. This includes tracking metrics like accuracy, response time, and user satisfaction to identify potential bottlenecks or errors.
• Evaluate the performance of the model on customer service data through rigorous testing and analysis. This requires identifying weaknesses, such as misclassification rates or contextual gaps, and proposing targeted improvements.
• Identify user issues across technical support, account queries, and security concerns by analyzing patterns in customer interactions. Develop solutions that address root causes and enhance user satisfaction through data-informed strategies.
• Document and report customer service data, training, and evaluation processes to ensure transparency. Share findings and recommendations with the customer service team to support continuous improvement initiatives.

Job Requirements

• Proficiency in data collection techniques, including web scraping, API integration, and manual data entry, to gather comprehensive customer service datasets.
• Strong analytical skills to clean and preprocess data, ensuring consistency, completeness, and relevance for machine learning applications.
• Experience with machine learning frameworks and tools to train, validate, and deploy models that improve customer service outcomes.
• Ability to interpret model performance metrics and translate them into actionable insights for refining customer service processes.
• Excellent communication skills to collaborate with cross-functional teams and present findings in a clear, concise manner.
• Knowledge of customer service best practices and industry standards to align data strategies with organizational goals.
• Attention to detail to ensure data accuracy and integrity throughout the entire lifecycle, from collection to evaluation.
• Problem-solving expertise to address complex customer issues and optimize model performance for real-world applications.
• Experience with data visualization tools to create reports and dashboards that communicate insights effectively to stakeholders.
• Ability to work independently and manage multiple tasks simultaneously while maintaining high standards of quality and efficiency.