Marian Dumitrascu

The model utilized transformer-based architectures to analyze vast amounts of market data and provided real-time insights and actionable intelligence for traders and investors, significantly enhancing their decision-making process. Key technologies used included AWS Lambda, S3, Redshift, and Kinesis for data ingestion and processing, as well as TensorFlow and PyTorch for model training and deployment. The project achieved a 15% improvement in prediction accuracy and a 30% reduction in time to insight for startup's clients.

Built a comprehensive dashboard that processes millions of data points daily, providing real-time predictive insights for customer churn, revenue forecasting, and user behavior analysis. Implemented automated ML pipelines that improved prediction accuracy by 25% and reduced manual analysis time by 80%.

Architected and developed a scalable BI portal handling 10TB+ of data across multiple client tenants. Implemented role-based access controls, real-time data synchronization, and advanced visualization capabilities. Achieved 99.9% uptime and 40% improvement in query performance through optimized data architecture.

Developed novel ML algorithms for processing and interpreting neural signals, achieving 30% improvement in signal classification accuracy. Contributed to proof-of-concept demonstrations and published internal research findings that influenced future R&D directions.

Successfully migrated over 50 business-critical applications with zero downtime, serving 10,000+ users. Implemented automated migration scripts, data integrity checks, and rollback procedures. Achieved 40% improvement in application performance and 60% reduction in infrastructure costs post-migration.