▪ Decrease 20% of Amazon Ads cost, by conducting optimal cost algorithm with sklearn module; doing A/B test, cross-functionally discussion and coming out with new process; and creating Pareto Chart and trending chart to indicate the worst 20% campaigns and keywords to be enhanced.▪ Contribute 95% of customer needs fulfillment, though creating algorithm with ARIMA module in Python to forecast and highlighting the critical products’ reordering with Tableau.▪ Lead the project of building up database, with Amazon Seller Central api and distributor EDI sales data, in SQL Server, and Dynamics 365 Business Central.▪ Offer business data analysis, identify technical solutions, and potential business process improvements in pursuit of driving efficiencies and meeting business needs.

▪ Improved $10M+ TWD quarterly sales by driving historical data time series forecast algorithm with statsmodels module, optimally sales target allocation, and cross-function alignment.▪ Cross-functionally developed integrated an AWS-powered end-to-end analytics platform.▪ Made day-to-day sales decisions around data, ensured that standards and policies are met, and be responsible for delivering trusted, high-quality reports.

* Improved working overtime by 25% and OTD release from 13% quarterly, by maintaining the database on Microsoft Access and SQL Server to monitor, analyzing designers’ hours and production by SSRS, and providing propriate training suggestion.* Classified Redline category by using NLP with Python - TfidfVectorizer for text strings, GridSearchCV for the best parameters, Pipeline for RandomForest, Naïve Bayes Models.* Reduced designer’s Redline by 75% quarterly, through creating analytical dashboard on Smartsheet, Power BI and Tableau, to analyze Redline distribution.

Fleet Services’ Fire Operations Maintenance ▪ Improved maintenance process and reduced the labor cost of Fire Operation Maintenanceby 20%, by visualizing data through dashboards in Tableau and helping City of Phoenixfinding critical problems of each section.▪ Cleaned and processed data with Python, including Numpy, Pandas, SKLearn; and didpipeline for RandomForestClassifier to categorize the maintenance parts.

•Analyzed customers’ data using SQL, SAP and Excel and reduced churn rate by 20% over the prior year.•Identified critical KPI and improved customer service’s outbound and inbound process to manage the outcomes.•Cooperated with customer service and IT to develop systems, and improved renewal rate from 20% to 30%.

• Planned and executed the annual events of the whole year.• Analyzed consumer behavior data by Excel and designed Direct Message with commodities and gift with activities of the promotion of Autumn Shopping resulting in an increase of sales by 34% compared to last year.