• Support financial planning and analysis, monthly closing, channel profit analysis, activities on sales team’s budget across product categories and develop financial strategy with ROI-based prioritization.• Analyze programs spends and develop & recommend improvements, and channel based on profit and ROI analysis, historical performance data, and company strategic initiatives.• Work with cross-functional teams to plan market and promotion spend allocation to validate ROI and alignment with company and strategic goals.• Collect bottoms-up financial forecasts for product categories for both target planning and management planning by account.• Support Category finance lead in overseeing overall sales team’s spend budget and shifts budget among channel to review final version with leadership• Institute improvements for budget/spend management across the organization, including data housing, communication, and forecast variance management• Perform program analysis of spend efficiency and identify areas to improve on marketing investments and provide senior leadership with regular reporting, budget status, financial metrics by category and channel spend analysis.• Own monthly target planning budget forecast process of marketing and promotional activities and submit monthly forecast of channel spending in both excel and internal N-ERP system.• Facilitates communication cross-organizationally across teams between channel marketing, account managers, corporate finance, marketing managers on program spends and budget amount.

- Being responsible for collecting, monitoring, and collating data in a potential risk sensing environment.- Discover and apply insights from data management, statistical analysis, and visualization capabilities.- Develop data analysis solutions based on descriptive analyses and predictive models.- Collaborate with Contact Center, Service Operation, Product Support teams to ensure that requirements for integration, security, data quality and cross functional usage are addressed.

- Tutor courses with the general knowledge of mathematics and statistics.- Communicate with the wide variety of students who come for help, and provide support for those who find themselves in academic trouble.
- Discuss the course material, focus on a specific problem, or give assistance on homework questions.

- Performed analysis in R with a data set that contained 256 features that could identify a fraud.- Made a spam filter involving text processing on a huge number of emails, and created a feature matrix.- Constructed different models using Random forest, Bagging, Boosting and Logistic Lasso.- Compared the models based on the misclassification rate, sensitivity, specificity, precision, and recall.

- Processed and analyzed flights and sales data from the flight management system.- Employed R to conduct multiple linear regression and principal component analysis methods to maximize operation revenue.- Managed international and domestic sales data, conducted statistical tests to optimize price discrimination strategy under various conditions.

- Ranked top 14.8% in the Kaggle competition with a score of 0.1308 (the lower the better)- Employed SAS to predict the final housing prices in Ames, Iowa, with 79 explanatory variables.- Implemented regression diagnostics for overfitting, heteroskedasticity and Multicollinearity issues. Cross-validation was used to estimate the accuracy of the model.- Applied random forest method to select optimal models with model selection techniques including LASSO, Stepwise, and Mallows Cp methods.

- Helped Buxton's client, Slacks on Racks department store, to select the locations they should open their new stores.- Conducted multiple linear regression in SAS, with the PCA method used to reduce the number of variables.- The Random Forest Regression Model with the misclassification rate was used to compare the confusion tables as well as the sensitivity and specificity of the model.