Accomplished QA Lead with a proven track record at UKG, enhancing operational efficiency and employee satisfaction through expert application of Selenium with JAVA and Agile methodologies. Spearheaded cross-functional teams, driving a 30% improvement in project delivery timelines. Renowned for exceptional problem-solving and team management skills, adept at fostering client relationships and advancing technological solutions.
Product Overview: Ticketmaster is a global platform for purchasing tickets to live events like concerts, sports, and theater. It offers event discovery, ticket sales, and event management services, with features such as mobile ticketing, real-time inventory tracking, and personalized recommendations. Ticketmaster simplifies access to events for consumers worldwide.
Automation Framework & Test Case Development: Designed, created, and maintained automation frameworks, automating test cases to enhance testing efficiency and coverage.
Requirements Evaluation & Test Plan Design: Evaluated project requirements and specifications to identify user/customer needs, and designed detailed test plans in TestRail based on those requirements.
Collaboration & Project Delivery: Collaborated with cross-functional teams to ensure quality across multichannel experiences and contributed to delivering brand-centric solutions in a full-service marketing agency.
Data Processing & Visualization: Expert in data preprocessing techniques, including handling missing values, duplicates, outliers, and imbalanced datasets. Proficient in feature scaling, feature engineering, and data normalization. Skilled in using Python, Java, TensorFlow, SQL, and data visualization tools like Tableau and Power BI. Experienced in using Matplotlib and Seaborn for univariate, bivariate, and multivariate analysis.
Machine Learning Algorithms & Statistical Methods: Proficient in supervised algorithms like Logistic Regression, Decision Trees, Random Forest, SVM, k-NN, and Naive Bayes for classification and regression tasks. Experienced in ensemble learning methods like Random Forest, Gradient Boosting, AdaBoost, and Bagging. Skilled in unsupervised learning using K-Means and PCA. Strong background in statistical methods, including probability theory, regression analysis, ANOVA, Bayesian statistics, time series analysis, and cluster analysis.
Model Deployment & NLP: Experienced in deploying machine learning models using Flask. Knowledgeable in Neural Networks, LLMs, and recommendation systems. Strong skills in NLP techniques such as Transformers, Hugging Face, GloVe, and vectorization. Familiar with Git and Jenkins for version control and CI/CD processes.
Applied Statistics Project: Utilized plotting distribution, visualization, and hypothesis testing to analyze industry problems and make data-driven decisions across various domains.
Supervised Learning Project: Applied popular classification techniques and extensive EDA to predict patient conditions and customer conversion for focused marketing.
Ensemble Techniques Project: Developed a machine learning model to predict customer churn for a telecommunications company, enhancing customer retention.
Unsupervised Learning Project: Implemented clustering and classification techniques to segment cars based on fuel consumption and classify vehicles from silhouettes.
Feature Engineering & Model Tuning: Used supervised learning and ensemble techniques to predict yield outcomes in semiconductor manufacturing, optimizing process efficiency.
Neural Networks & Deep Learning: Delivered two sub-projects, including a regressor/classifier for equipment signal quality prediction and an image classifier for street-level number
Date of Birth: 07/13/91