Sidi Mohamed EL Jeilany

– Designed and implemented Amazon Sales & Shares estimation models, achieving industry-leading accuracy. – Led a global cross-functional team of engineers and researchers in ML, MLOps, and data science for NielsenIQ's Digital Shelf platform. – Developed a distributed data processing pipeline, processing up to 1.38TB/ day while reducing costs by 80%.

Adjunct Lecturer teaching a practical Master's-level course with Professor Dario Colazzo on Big Data Processing and Distributed Systems. – Engaged students through hands-on lab sessions utilizing Advanced SQL, Hadoop, Spark, and Cloud clusters. – Guided students in applying shared-nothing parallelism and MapReduce paradigms for scalable algorithm development. – Addressed industrial use cases, emphasizing performance and fault- tolerance in big data pipelines.

– Directed R&D strategy for AI-driven retail analytics solutions in a fast-growing startup environment. – Managed and mentored cross-functional teams, enhancing skills in data engineering and machine learning. – Oversaw the implementation of scalable AI models, improving analytics capabilities across departments. – Led the migration of real-time Retail KPIs APIs to Golang, enhancing performance and reliability.

– Designed and implemented a distributed data processing platform utilizing CoreOs, Node.js, Redis, and MongoDB. – Developed and deployed multiple ETL pipelines for high-volume dataset management from diverse sources. – Contributed to the architectural overhaul of internal APIs, enhancing performance with a new data aggregation layer in C/C++. – Led performance optimization of legacy PHP systems, significantly improving throughput and reducing latency. – Played a key role in transitioning the organization toward modern cloud based data architectures and AI-driven analytics.