• Hauls product from the receiving dock to the appropriate aisle for the lift operator to put away. • Perform work tasks in a safe manner in accordance with work instructions. • Proactively take actions to improve safety.

• Accurately complete assignments and resolve issues related to accuracy order selection (Full case, Flow, Pick-to-belt, Palletizer). • Select orders for shipment in an organized, clean, accurate and damage free manner. • Properly tag orders with defined shipment information. • Complete work assignments in a timely manner and use time in an efficient way in support of facility goals for on-time release of trailers. • Take a proactive approach to assist fellow Partners through their own actions, such as sharing of information (i.e., technical, facility specific knowledge, processes & procedures). • Perform work tasks in a safe manner in accordance with work instructions. • Proactively take actions to improve safety.

• Partnered in the development, validation, testing, and implementation of churn prediction applications using Java.• Engaged in the assessment of churn risk models by running SQL queries on big datasets under KNIME environment.• Experienced using SQL to extract and transform data for exploratory analysis in SPSS and trends visualization in Tableau.• Designed a churn prediction model by building a decision tree implemented in SQL, identifying 70% of potential defecting customers.• Combined Support Vector Machines with feature selection strategies to train a customer churn model using the LIBSVM library, achieving an 80% reduction of input variables.• Trained an Artificial Neural Network (ANN) model using the Scikit-learn Python library to predict customer churn and compare its accuracy with other supervised learning algorithms such as Logistic Regression.• Made progress on the development of a model to improve cash demand forecasting for a bank ATM network using ANNs and Fuzzy Logic, allowing the institution to reduce expenses in cash transportation by 30%.• Blended feature engineering and customer lifetime value to enhance the accuracy of a churn model by 15% under RapidMiner workflow.• Implemented Excel Solver to optimize predicted probability cut-off value of churn classification models and deliver product usability to our clients.• Handled class imbalance in a churn prediction model by crafting an appropriate evaluation metric, beating the accuracy of other classifiers by 20%.• Investigated research papers and various theses to compare state-of-the-art practices of customer churn prediction in the telecommunications industry with the effectiveness of classic methods in the literature.

• Analyzed customer metrics behavior using clustering in SPSS to anticipate loan delinquency and default.• Headed the development of a client portfolio assessment using a scoresheet created with K-means algorithm in RStudio.• Contributed to increase volume of loans using customer performance analysis in Excel, growing revenue of the Private Banking division by 25%.• Developed customer segmentation models using hierarchical clustering in R to identify new market segments, which Banesco drew upon to offer more products and services successfully.• Applied Principal Component Analysis using R to reduce dimensionality of the customers’ dataset by 65%.• Created reports to evaluate customer equity of the Private Banking division using Pivot Tables and VLookup in Excel.• Conceptualized and implemented an evaluation method in Excel to rate customers value based on their credit score.