Jason Beasley

Responsible for developing, maintaining, and automating complex AML reports to mitigate risk, enhance efficiency, and ensure regulatory compliance.

• Transformed 16 time-intensive manual reports into fully automated Python processes, increasing efficiency by 90% and eliminating human-induced errors.
• Spearheaded multiple reporting projects from inception to completion, ensuring alignment with business objectives and stakeholder needs.
• Developed and deployed 37 interactive Tableau dashboards to empower executive, senior leadership, and key stakeholders with data-driven insights.
• Fostered cross-functional collaboration to optimize reporting processes and deliver data-driven solutions that supported multiple lines of business.
• Led a successful migration of 50+ reports from legacy systems to Snowflake, improving data accessibility, performance, and scalability.
• Architected and executed a large-scale QA/QC initiative, streamlining processes and enhancing data quality through the integration of Python, SharePoint, Snowflake and Tableau.

Design, develop, and maintain complex data warehouse solutions to support business intelligence and risk management initiatives.

• Led cross-functional teams in consolidating 13 disparate source systems into a unified commercial risk database, improving data accessibility and consistency.
• Developed and implemented robust SAS data quality control scripts to ensure accuracy and integrity of critical data elements, mitigating risk and enhancing data reliability.
• Designed and built a comprehensive SAS-based general ledger reconciliation process, automating manual tasks and providing timely, accurate financial reporting.
• Collaborated with development teams to optimize data mapping and modeling, aligning data structures with business requirements and improving data quality.
• Leveraged Alteryx to streamline data acquisition, cleansing, and reporting processes, enabling efficient response to ad-hoc requests and improving overall reporting efficiency.

Leverage advanced analytics and Python to develop and deploy interactive Tableau dashboards, optimizing credit risk management and decision-making.

• Streamlined reporting processes by automating numerous complex manual reports using Tableau, Python, and Power Pivot, increasing efficiency and data accuracy.
• Accelerated report delivery by reducing Concentrations MRA report turnaround time by 75%, enabling timely decision-making and meeting critical deadlines.
• Enhanced risk management framework by developing and implementing new RMII desktop procedures, improving operational efficiency and compliance.
• Provided actionable insights during the COVID-19 crisis by creating and delivering timely, data-driven reports to support business continuity and risk mitigation.
• Optimized data accessibility by creating and managing high-traffic tables and views, supporting efficient data analysis and reporting across the risk function.

Drive operational efficiency and mitigate risk through data-driven analysis, automation, and process improvement.

• Streamlined reporting processes by automating 22 manual reports, achieving zero-touch efficiency and significant time savings.
• Ensured regulatory compliance by managing 35 monthly/quarterly reports, meeting strict SLAs and adhering to internal/external requirements.
• Optimized data retrieval by streamlining complex SAS and Teradata SQL queries, enhancing data efficiency and accuracy.
• Fostered cross-functional collaboration as primary data supplier to CCB Risk, driving development of a centralized risk database.
• Mitigated business risk by creating a report to proactively identify customers impacted by natural disasters.
• Improved data governance by restructuring BRD and metadata archives, enhancing data accessibility and transparency.

Drive data-driven decision making by collaborating with cross-functional teams to optimize forecasting processes and systems.

• Implemented a robust defect tracking process to improve data quality and system performance, resulting in 35% reduction in human errors.
• Developed and optimized SQL queries to extract and transform data from multiple Demantra databases, enhancing data accessibility for forecasting and analysis.
• Created and managed a comprehensive data repository with over 500 custom fields, empowering inventory management analysts with critical data insights.
• Leveraged historical data analysis to identify trends and patterns, resulting in the development of a predictive model that improved forecast accuracy by 47%.