Retail Data Analysis
Pubblicato il 08-09-2026 - 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.