Data Scientist

Pioneered a real-time monitoring dashboard for search metrics, instantly alerting the team to anomalies and performance issues, which reduced critical downtime by 40% and contributed to halving the number of analysts on duty. Automated data processing workflows in Python, cutting data latency by 50% and enabling faster, more accurate insights that allowed the team to respond to search trends in real time, ultimately improving search relevance by 3%. Designed and executed large-scale A/B tests on search algorithm adjustments, which led to an 8% increase in search-to-purchase conversions, driving significant revenue growth and validating key hypotheses for continuous search improvement.

Developed automated fraud detection models leveraging machine learning, computer vision, and NLP techniques to identify suspicious patterns in real time, achieving a 30% reduction in fraud-related losses in the image fraud category and enhancing overall security. Optimized data-driven decision-making by creating a dashboard with key metrics using PostgreSQL/Greenplum, leading to a 20% improvement in metric tracking accuracy and enabling faster, more effective quality control decisions. Implemented a custom Traveling Salesman algorithm in Python to streamline logistics analysis, reducing operational costs by 12% and significantly improving delivery efficiency for high-priority customer segments.