Lenovo

Job Responsibilities:

1. Understand analysis requirements and evaluate viability from data analysis point of view.

2. Understand the data structure and suggest with later analysis requirements.

3. Perform exploratory data analysis (EDA) to uncover initial insights and patterns.

4. Identify trends, correlations, and insights that answer the business questions.

5. Validate findings with statistical significance and reliability.

6. Implement machine learning models or statistical methods to analyze data and extract meaningful patterns.

7. Optimize algorithms to enhance the efficiency and accuracy of analysis.

8. Interpret the analysis results, connecting them back to the original business problem.

9. Review visualizations to ensure they meet business needs and are easily interpretable by the target audience. Suggest modifications or additional visuals based on stakeholder feedback.

10. Ensure all data analysis steps are well-documented. Identify any gaps, errors, or areas for improvement in the analysis process. Prepare and present the final analysis report to stakeholders, incorporating their feedback as needed.

Job Requirements:

1. Ability to understand business problems and translate them into analytical objectives.

2. Proficient in performing exploratory data analysis (EDA) to uncover trends, distributions, anomalies, and correlations.

3. Skilled in using tools like Python (Pandas, Seaborn), R, or SQL to perform data slicing and summarization.

4. Comfortable with statistical validation methods (e.g., hypothesis testing, confidence intervals) to confirm robustness of findings.

5. Experience implementing machine learning models, statistical models, or optimization algorithms to generate predictive or prescriptive insights.

6. Ability to evaluate model performance and tune parameters for improved accuracy and relevance.

7. Understand when and how to use supervised/unsupervised models or statistical testing techniques.

8. Able to interpret analysis results and explain them in the context of the original business problem.

9. Bridge the gap between technical analysis and business understanding, offering practical and actionable recommendations.

10. Proficient in visualization tools such as Power BI, or programming Python libraries like Plotly, ggplot2, or Matplotlib.

11. Capable of tailoring visuals to different audiences and modifying visualizations based on stakeholder feedback.

12. Identify and flag any analytical risks, data quality issues, or gaps during the process.

13. Open to Several Locations: Tianjin, Beijing, Shenzhen