• Created a real-time fraud detection pipeline using Python, Scikit-Learn, and TensorFlow, decreasing fraud incidents from 20,000 to 12,800 per month by leveraging adaptive model training and real-time monitoring.• Crafted a data-driven performance dashboard using React and D3.js, which visualized real-time analytics and KPIs for 20+ clients, enabling clients to track key metrics and leading to a 25% increase in data-driven decision-making.• Developed an anomaly detection system for transaction processing in Spark and Scala, enhancing detection accuracy for 50 million daily transactions and reducing false-positive alerts from 6 million to 3 million per month.• Built and deployed NLP-driven customer support tools using Python, NLTK, and SpaCy to handle up to 25,000 customer queries monthly, cutting average response times from 2min to 15s, improving customer satisfaction.• Optimized payment processing latency by designing AI-based load-balancing algorithms, cutting transaction times from 1.2 seconds to 0.7 seconds on average for 100,000 daily users, which raised customer satisfaction scores from 84 to 92.• Launched a recommendation engine for cross-sell opportunities using collaborative filtering models in PyTorch and Spark, contributing to an annual revenue boost of €1.3 million by identifying and targeting 50,000 high-value customers.

• Redesigned patient data processing services on a HIPAA-compliant cloud infrastructure (AWS Lambda, Node.js, React) to increase record processing capacity from 8,000 to 20,000 per day, streamlining patient data access for 15 hospitals.• Designed and built a diagnostic support tool powered by CNNs in Keras and TensorFlow, reducing diagnostic processing time from 2min to 3s for each of 1,500 doctors, supporting faster decision-making and improving patient care outcomes.• Implemented a predictive appointment scheduling model using Python and XGBoost, decreasing missed appointments by 2,500 monthly across 10,000 scheduled visits, which resulted in greater clinical availability and patient attendance.• Converted monolithic architecture to microservices using Docker and Kubernetes, raising uptime from 96.5% to 99.8% and supporting a user base expansion from 30,000 to 55,000 patients without additional server costs.• Automated data analytics reporting using Pandas and SQL, providing real-time patient outcome insights for 200 medical centers, reducing manual reporting time from 80 hours to 20 hours monthly.

• Developed a scalable custom CRM platform (React, Fast API, PostgreSQL) that streamlined client management for 15 high-profile clients, reducing onboarding time per client from 4 hours to 2.5 hours, increasing team productivity by 60%.• Designed a predictive lead scoring model in Python with Scikit-Learn, boosting annual qualified leads from 1,500 to 1,620 and resulting in €500,000 in additional sales by better prioritizing high-potential leads.• Migrated legacy infrastructure to a cloud-based system on AWS EC2 and RDS, cutting annual downtime by 55 hours and decreasing site load times from 3.5 to 1.8 seconds, improving client experience and retention.• Developed high-performance RESTful APIs with Django and Flask, integrated with third-party services, handling over 200 requests per second to support real-time data processing and insights for clients.