Search Intent Classification Modeling Project• Created NLP classification models that bucket any search query into groups based on client-defined taxonomy to glean insights on what queries the client is winning, wasting on, and have the potential to grow on• Implemented data pre-processing using tokenization and count vectorizer with stemming and lemmatization then trained the data with classification models Anomaly Detection Project• Built an anomaly detection machine learning model using Prophet package in python for various digital marketing KPIs (key performance indicators) such as impression, clicks, conversion, clickthrough rate, and cost per click• Automated the anomaly detection processes to run every day from pulling data in google bigquery to delivering anomaly notifications in slack using cronjob

• Produced about five reports each week on the Freeosk programs at Sam’s Club and Walmart• Analyzed customer inquiries related to the Freeosk program, including A/B testing, ROI analysis, abnormal program results, and operational issuesFreeosk User Engagement Predictive Modeling Project • Developed machine learning models in python to predict the number of engagements in Freeosk programs using multiple linear regression models, random forest, and XGboost • Implemented data pipelines and automated modeling processes from data extraction, data cleansing and manipulation, model building, and report creationSam’s Club Traffic Predictive Modeling Project • Created a time series model to predict the number of traffic of all 589 Sam’s Club stores• Automated the modeling process in Python, from importing data into SQL to utilizing modeling technics such as detecting and removing outliers, linear regression interpolation, and predicting traffic using the TBATS time series model • Developed report builder application in Python Dash with the team from data extraction, cleansing, building logic to report metrics, and extracting insights

Capstone Project • Worked with the largest bell pepper producer in Mexico to optimize and forecast production • Analyzed the company’s harvest data using nonlinear mixed effects model to predict accumulated yield over harvest season in RAirBnB Predictions Project • Modeled listing prices and customer reviews using logistic regression with lasso regulation and random forest regression in R• Cleaned raw data and identified important variables for predictive models then validated model accuracy using 5-folds cross validationAutoComplete Me Project • Implemented Autocomplete function in Python and designed complex algorithms using object oriented programming, recursion, and creating Trie data structure • Tested the performance of the functions on various input sizes using profiling tools in Python

▪ Composed daily and weekly market analysis of agricultural futures on the Chicago Board of Trade (CBOT), based on fundamental factors ▪ Analyzed seasonal trends of transportation cost to develop cost-efficient origination and marketing strategies▪ Oversaw the purchase, transportation, and marketing of all commodity transactions▪ Coordinated in-land transportation, transloading, and ocean freight simultaneously to export 150,000 tonnes of grain and feed products per year to various Asian destination markets▪ Consulted on claims and issues with customers to initiate appropriate settlement process and minimize financial losses▪ Developed and maintained superior working relationships with all suppliers and customers