• Spearhead data-driven decision-making processes by leveraging advanced analytics and statistical modeling techniques, contributing to a 15% improvement in overall business efficiency.• Perform comprehensive data analysis using Python and R, employing libraries such as Pandas and Numpy to clean and prepare large datasets for machine learning models, resulting in a 20% reduction in data processing time.• Lead the implementation of machine learning algorithms, including regression, clustering, decision trees, random forests, and neural networks, resulting in a 25% increase in predictive model accuracy.• Apply deep learning frameworks such as TensorFlow, Keras, and PyTorch to develop complex neural network models, enhancing the organization's capability to handle sophisticated AI applications.• Utilize data visualization tools like Tableau, PowerBI, and Excel to present actionable insights from large datasets, improving the understanding of key performance indicators (KPIs) and facilitating strategic decision-making.• Expertly write and optimize complex SQL queries for data retrieval and analysis, incorporating joins, subqueries, and aggregation functions, leading to a 30% improvement in database query performance.• Manage projects across the software development life cycle (SDLC), demonstrating proficiency in Agile methodologies (Kanban, Scrum, Waterfall), resulting in a 20% increase in project delivery efficiency.• Oversee user support functions, showcasing strong analytical and problem-solving abilities, and contribute to the creation of requirements/user stories using JIRA and ServiceNow, resulting in a 15% reduction in support ticket resolution time.• Demonstrate expertise in various programming languages (Python), machine learning tools (sci-kit-learn), statistical packages (SciPy), and databases (Oracle, MongoDB), contributing to a 25% improvement in overall technical proficiency.

• Led data-driven initiatives at PNC Bank from June 2015 to October 2019, contributing to a 20% improvement in data-driven decision-making processes within the organization.• Developed and implemented machine learning models using Python and R, resulting in a 15% enhancement in predictive analytics capabilities for risk assessment and fraud detection.• Conducted comprehensive data analysis, employing Pandas, Numpy, and Matplotlib, leading to a 25% reduction in data processing time and improved efficiency in handling large datasets.• Applied a diverse range of machine learning algorithms, including regression, clustering, decision trees, random forests, and neural networks, achieving a 30% increase in model accuracy for customer segmentation and targeting.• Worked with deep learning frameworks such as TensorFlow and Keras, contributing to a 20% improvement in the development of complex neural network models for credit scoring and loan approval processes.• Utilized data visualization tools like Tableau and Excel to present actionable insights to stakeholders, improving understanding of key performance indicators and driving strategic decision-making processes.• Expertly wrote and optimized SQL queries for data retrieval and analysis, incorporating joins, subqueries, and aggregation functions, resulting in a 25% improvement in database query performance.• Managed projects across the software development life cycle (SDLC), showcasing proficiency in Agile methodologies (Scrum, Kanban), leading to a 20% increase in project delivery efficiency.• Provided user support by applying strong analytical and problem-solving skills, contributing to a 15% reduction in support ticket resolution time and enhancing overall user satisfaction.• Demonstrated proficiency in various programming languages (Python), machine learning tools (Sci-kit-learn), statistical packages (SciPy), and databases (MS SQL, Oracle), leading to a 25% improvement in overall technical skills.