Retail Data Analysis
Pubblicato il 07-09-2026 - Xiaomi Technology in Milano
Job Description:
Data Management
Take overall responsibility for the accuracy, completeness, and timeliness of retail data.
Ensure SO (Sales Out) information is entered into RMS system completely, accurately, and on time, including:
Organization and retailer master data
Store master data
Employee/store relationship data
Store-retailer relationship data
Business rules, including sales, inventory, assortment, and expense calculation rules
Ensure RMS data remains complete, accurate, and up to date.
System Implementation & Adoption
Follow the implementation roadmap and drive the adoption of key data functionalities within the region.
Support the rollout and adoption of new modules, features, and business processes.
Ensure new RMS functions are implemented and effectively used by local teams within the required timeline.
Issue Resolution & Continuous Improvement
Collect and manage local business and system issues, including:
System performance issues
Functionality issues
User experience issues
Business process issues
Provide timely feedback and follow up with the relevant teams until issues are resolved.
Continuously identify opportunities to improve system functionality and business processes.
Key Technical Skills
Data Analysis & Visualization
Proficient in Python for data analysis, with hands-on experience using Pandas, NumPy, and Matplotlib/Seaborn.
Able to perform data processing, analysis, and visualization.
Familiar with commonly used data analysis methodologies.
SQL
Proficient in MySQL and SQL query development.
Capable of independently extracting, processing, and analyzing data from databases.
Able to use Power BI to connect to and visualize data from multiple sources and build complete dashboards and analytical reports.
Excel
Proficient in advanced Excel functions.
Experienced in creating PivotTables, charts, and other analytical reports.
Able to efficiently handle daily data analysis and reporting tasks.
Language
Fluent in Italian, Chinese and English.