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Experience Level:
Senior
Desired Job Role:
Data Engineering
Experience:
At PNC Financials, my role as a Data Analyst involved leading and implementing several high-impact data initiatives. One of my key responsibilities was developing an AI-driven credit risk assessment model. This model improved prediction accuracy by 25%, which directly contributed to reducing the company’s potential losses by $5 million annually. I was responsible for the end-to-end process, from data collection and cleaning to model development and deployment.
Another major responsibility was architecting a scalable data pipeline using AWS Lambda, which processed over 2 million daily transactions. This significantly boosted our operational efficiency by 40%. I collaborated closely with cross-functional teams to ensure that this pipeline seamlessly integrated with our existing systems.
I also spearheaded the implementation of a machine learning-based fraud detection system. This system identified fraudulent transactions in real-time, reducing them by 30% and saving the company $2 million annually. It required a deep understanding of data patterns and advanced machine learning techniques.
In addition, I designed executive-level dashboards in Tableau, which were pivotal in enhancing data-driven decision-making at the executive level. These dashboards eliminated the need for 20 hours of manual reporting each week, allowing our leaders to focus more on strategy.
I also led the development of a customer segmentation model that increased our marketing ROI by 20% through more targeted campaigns. This project involved using clustering techniques to better understand our customer base and refine our marketing strategies.
Throughout my time at PNC, I focused on optimizing big data processing workflows, which reduced query execution times by 50% and significantly improved system performance. I also implemented a real-time analytics platform using Streamlit, which reduced our response time to market changes by 60%, giving us a competitive edge.
Finally, I took the initiative to establish a data quality framework using Python, reducing data errors by 45% and improving data integrity. I also led knowledge-sharing programs that increased team productivity by 20% through the dissemination of best practices and advanced data techniques.
Another major responsibility was architecting a scalable data pipeline using AWS Lambda, which processed over 2 million daily transactions. This significantly boosted our operational efficiency by 40%. I collaborated closely with cross-functional teams to ensure that this pipeline seamlessly integrated with our existing systems.
I also spearheaded the implementation of a machine learning-based fraud detection system. This system identified fraudulent transactions in real-time, reducing them by 30% and saving the company $2 million annually. It required a deep understanding of data patterns and advanced machine learning techniques.
In addition, I designed executive-level dashboards in Tableau, which were pivotal in enhancing data-driven decision-making at the executive level. These dashboards eliminated the need for 20 hours of manual reporting each week, allowing our leaders to focus more on strategy.
I also led the development of a customer segmentation model that increased our marketing ROI by 20% through more targeted campaigns. This project involved using clustering techniques to better understand our customer base and refine our marketing strategies.
Throughout my time at PNC, I focused on optimizing big data processing workflows, which reduced query execution times by 50% and significantly improved system performance. I also implemented a real-time analytics platform using Streamlit, which reduced our response time to market changes by 60%, giving us a competitive edge.
Finally, I took the initiative to establish a data quality framework using Python, reducing data errors by 45% and improving data integrity. I also led knowledge-sharing programs that increased team productivity by 20% through the dissemination of best practices and advanced data techniques.
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