Over a three-year period, we worked closely with one of Russia's largest banks to successfully establish a robust AI monitoring system. This system operates with precision to swiftly address system failures by pinpointing their root causes and making well-informed decisions. The model expanded the scope of forecasted indicators to a total of 50,000, achieving an 85% reduction in false positive alerts. It also delivered a 20% improvement in precision compared to previous methodologies.

Led a team to develop an AI monitoring system that was capable of detecting anomalies in various business indicators. By closely working with one of Russia's largest banks over a span of 3 years, we designed and implemented an advanced backend system dedicated to the detection of anomalies across a wide spectrum of critical business metrics. This system significantly decreased the time and effort previously invested in manual incident detection and enhanced operational efficiency.

• Organized pre-launch product UX testing, regular users research and new features backlog prioritization

• Organized the company's participation in high-profile contests, resulting in Nimb becoming a winner of Smart City Expo worldwide Congress’s call for solution in the “Safe Cities” category and a finalist of Xprize Women’s Safety competition

• Set up efficient client support

Led the shift of analytical infrastructure, enhancing data resources and decreasing update errors by 90%. Increased identifiable traffic sources from 60% to 95%. Played a crucial role in reducing fraudulent responses from 28% to 1-4% by strengthening moderation and creating a fraud analytics direction. Contributed to a project reducing the cost of applications from groups by 70% and reducing vacancies in central Moscow from 20% to 6%.