Oversee all data analytics and processing initiatives on sales KPI data management and pipeline automation contributing 20 million of cost savings.• Development of KPI Q&A Chatbot (WIP): Spearheaded the conceptualization and execution of a KPI Q&A chatbot hosted on Microsoft Azure cloud. Utilized the cutting-edge Retrieval Augmented Generation (RAG) model framework, integrating the open-source Llama2 LLM for enhanced functionality.• KPI Data Automation Pipeline: Engineered an automated data process to streamline KPI data collection, replacing manual methods. This initiative not only drove digital transformation but also resulted in significant cost reductions by 50%. Additionally, developed and managed a robust KPI data pipeline management system.• Intel Sales KPI Management: Orchestrated the comprehensive management of Intel's global sales KPI framework and sales organization hierarchy. Collaborated closely with all Business Units and Regions to establish semi-annual KPI targets, ensuring alignment with organizational objectives.• Intel Sales KPI dashboard: Managed product feature improvements of the Intel Sales KPI dashboard. This critical tool provides real-time insights into sales performance, facilitating informed decision-making and strategic planning.

Developed 6+ data science/AI tools/platforms for Intel Sales.• Tender AI: Pioneered the development of an AI platform, Tender AI, designed to intelligently identify lucrative sales opportunities from publicly available projects published online by government and public institutions. This groundbreaking initiative resulted in a remarkable 21% conversion rate and an impressive quarterly revenue impact exceeding $100 million.• SMG Sales AI Customer Selection Tool: Collaborated in creation of an AI-driven Account Coverage Scoring Model for Intel's Covered Accounts within the SMG Sales division. This innovative solution utilized various account criteria data and AI scoring model to optimize selection processes. Recognized as a Division Recognition Award-winning project, it was successfully adopted and implemented by SMG Sales, driving enhanced efficiency and strategic decision-making.• Intel Sales Assist: Involved in the development of Sales AI engine "Sales Assist," seamlessly integrated into the Salesforce CRM platform. Leveraging data mining and web crawling techniques, Sales Assist provided actionable insights and recommendations to Intel sellers, significantly enhancing sales strategies and contributing to an annual revenue impact exceeding $200 million.

Administrated 8+ data science initiatives in marketing analytics, pivotal in providing valuable insights to senior management and improving marketing strategies. • Sales Propensity to Buy AI Model: Led a team of data scientists to build a Propensity to buy Model on the likelihood of B2B accounts to purchase Intel server products and deployed in an insights dashboard serving as an extension of Salesforce platform for sales, with opportunity creations boosted by 10%; Selected and presented in 2020 Intel software Professional Conference. • Finance Budget Allocation Optimization Model: Collaborated with Finance on Direct Marketing Budget Allocation Optimization across 30 countries, enhancing ROI with Intel finance and revenue data, based on linear programming algorithm.• KPI analysis with Bayesian Network: Built a Bayesian Network for KPI analysis to identify key drivers of consumer preference for high-end Intel Gaming CPU by using Intel Gaming consumer survey data, refining marketing strategies.• Customer Churn Analysis Model: Developed a Customer Churn Model for Intel Technology Provider (ITP) program to find out the drivers that lead to membership downgrade/loss using customer sales and membership behavioral data.

Partnered with multiple Intel Business Units on various data analytics projects, which facilitates business partners with delivering impactful analytical tools and functionalities. • Intel Global Customer Support Warranty Return Prediction Model: Led the implementation of a customer support warranty prediction model utilizing XGBoost, effectively reducing agent troubleshooting time and increasing customer satisfaction rates. This initiative resulted in a 15% efficiency improvement and $30 million in savings. Recognized with a white paper selection at the prestigious 2020 Intel Technical Leadership Conference and subsequent Intel patent publication.• Intel Global Customer Support Text Analytics Platform: Spearheaded the development of a unified Customer Support text analytics platform leveraging advanced Natural Language Processing techniques such as topic modeling, search clustering, and word tree analysis. This innovative platform empowered Intel to monitor customer sentiments, interpret satisfaction levels, and proactively address customer complaints and feedback, driving enhanced customer experiences and loyalty.• Intel Salesforce Opportunity Win Likelihood Scoring: Led the productionization of an end-to-end Logistic Regression model for Salesforce Opportunity Win likelihood scoring. This critical solution provided Sales teams with valuable insights to prioritize opportunities effectively, contributing to improved sales strategies and revenue growth by an estimate of $50 million quarterly.• Data Science Process with Lean Six Sigma Methodology: Designed an enterprise data science process using Lean Six Sigma methodology, which was widely adopted by Intel IT Data Science communities.• Intel Finance WW Revenue Forecasting Model: Developed a time series-based Global Revenue Forecasting model for Intel Finance, delivering exceptional accuracy with a MAPE within 3%, adopted into Intel's quarterly earnings reporting, and was used in earning calls for investors.

• Conducted building energy consumption forecasting using Random Forest with Hadoop, MapReduce, Python and Spark MLlib, resulting in 10% accuracy improvement using Big Data Analysis and Parallel & Cloud Computing.• Integrated the implementation in the cognitive computing systems of “Green Horizon”, a collaborative project with Beijing municipal government, in which urban air quality was significantly improved within one year.• Hosted a Big Data seminar and presented research findings;

Co-authored a paper in which an automated computing machine learning model for patients’ excessive radiation exposure detection on CT examinations was developed (A featured journal article), with R & MATLAB.