1. Led analytics team of 4 responsible for accelerating revenue growth globally by developing up-sell and cross-sell recommendations and scaling usage driven monetization for Sales with hypothesis testing, statistical modeling and Airflow. Generated $50+ million revenue in 2022.2. Designed team roadmap based on Product’s and Sales’ requirements. Increased the number of sales analytic programs by 50% and generated additional $14 million revenue comparing to 2021. 3. Developed LightGBM model in Python to identify potential customers for product upgrade. Leveraged SHAP to interpret model predictions. Created customer specific talk tracks for account executives. Generated $3 million revenue with 40%+ increase in conversion rate comparing to rule-based model. 4. Designed profitability framework for SMS messaging products. Created Tableau dashboards to monitor key profitability metrics and identify 280 underpriced customers for Product, Finance and Pricing teams. Provided pricing guidance based on customers’ usage patterns.

1. Leveraged data analytics to identify potential customers for a wide range of products, sent out targeted cross-sell/up-sell recommendations to account executives. Generated $5 million revenue in 2020.2. Developed XGBoost model in Python to identify cross-sell opportunities for Platform product. Leveraged partial dependence plot to interpret model predictions and create customer specific talk tracks for account executives. Generated $1 million revenue with 20% increase in conversion rate.3. Utilized regression model and permutation test with Bonferroni correction to identify sales price as the key driver in the creation of special SKU and determine the sales price thresholds. Generated special SKU creation guidance for net new product launches. 4. Developed Tableau dashboards to monitor the impact of COVID 19 on product sales price as well as transaction volume and highlight deals with unusual discounts. Refreshed the dashboards and created weekly email summary for executive leadership (EVP, SVP). 5. Configured and managed Airflow on Linux server to automate ETL data pipeline to support key tables update and dashboards refresh.

1. Leveraged visualization and statistical testing packages in R to analyze correlation between Facebook news feed ads character length and relevance score as well as CTR. Identified potential optimal character length for ad components which led to 20% increase in CTR.2. Configured and managed EC2 Server and MySQL databases on AWS to extract, process and store data for real time reporting on Data Studio and Tableau (ETL pipeline). 3. Utilized R to extract leads and enrollment data from PostgreSQL database. Analyzed lead to enrollment cycle with ggplot. Created enrollment forecasting with regression model for channel optimization.4. Developed real time and interactive Tableau dashboards by merging(Join/Blend) CRM data from PostgreSQL database with budget data from Google Sheet for internal performance evaluation.5. Designed data structure for external reporting on Data Studio. Leveraged R to automate manipulation of data from PostgreSQL and different vendor platforms.6. Created functions in R to attribute spend of Google Ads, Facebook Business Manager and other vendor platforms to leads; uploaded CPL (Cost Per Lead) data to MySQL database on AWS.

1.Developed R algorithm to calculate CPL(cost per lead) and CPE (cost per enrollment) with data from Salesforce and Facebook Adverts Manager. 2. Leveraged Google Analytics to evaluate the performance of Facebook Apps campaigns across 3 brands; created evaluation reports with Google Data Studio.3. Generated detailed consumer data from Salesforce; Employed Pivot Table in Excel to analyze customer demographic information across 23 campuses.

1. Managed design of electronic reading products; launched three products in August.2. Conducted quality review and generated diagnostic reports for five digital products; assisted colleagues to improve products performance.