● Credit Risk Modeling Software: Engineered and maintained credit risk modeling software for Deep Future Analytics, achieving a 30% improvement in model accuracy and a 25% speed up and upgraded updating process, enhancing overall performance and reliability. ● Data Loader Optimization: Introduced multiple versions of a data loader tool, ensuring data integrity, eliminating inconsistencies by 35%, which led to more reliable and accurate data insights. ● Internal Systems Maintenance: Maintained and enhanced internal logging and billing systems, adding new functionality that upgraded real-time data processing and reporting, resulting in a 20% increase in operational efficiency. ● Backend Development: Utilized Flask, RestAPI and GraphQL to build scalable backend solutions for internal projects, improving API response times by 40% and ensuring robust system performance. ● AI Commenting Tool Enhancement: Increased scenario coverage by 67%, improving performance and accuracy by 25% through ChatGPT API integration.

● Enhanced Tax Compliance: Customized Microsoft Dynamics 365 to support tax reporting across multiple countries, ensuring compliance with local tax legislation and eliminating inaccuracies in tax filings by approximately 15%. ● Proposed Application Features: Designed and produced scalable application capabilities, resulting in a 10% increase in user satisfaction and a notable reduction in support tickets. ● Streamlined Code Management: Prepared and debugged product and test code on a distributed platform using C# and X++, leading to a 12% decrease in system downtime and increased application performance. ● Peer Code Review Leadership: Conducted peer code reviews across teams, contributing to a 20% reduction in production bugs and maintaining high code quality standards. ● Successful Localization: Implemented VAT declarations and reports for Europe, Russia, and Brazil, enhancing localization functionality and supporting accurate tax reporting across these regions.

● Advanced Predictive Analytics: Implemented Kalman filtering in Python using Pandas, SciPy, NumPy, and statsmodels to predict yield curves, improving predictive accuracy by 20% and enhancing the bank’s forecasting capabilities. ● Enhanced Data Validation: Created Python-based validation processes for economic data, enhancing precision in financial reporting by 15% and reducing data inconsistencies. ● Optimized Analytical Tools: Utilized machine learning and statistical methods to streamline data analysis workflows, accelerating report generation times by 25% and providing more actionable insights for financial decision-making.

● Designed a database to store detailed information about arbitration managers, enabling efficient data retrieval and management. ● Built a WPF desktop application using C# to process reports, reducing the reporting time from one week to two days, achieving a 71% improvement in efficiency. ● Created a Python-based web scraper to automatically collect and upload arbitration manager data into the database, eliminating manual data entry and reducing human errors.