• Conducted comprehensive research into big data analysis and data mining techniques within NoSQL (Graph) databases, culminating in the creation of inventive solutions for handling large graphs and Multilayer Networks, thereby enhancing scalability.• Enhanced mining algorithms for substructure discovery on large graphs, yielding a 46% speed improvement over the existingapproach and a 24% reduction in server resource utilization.• Conceptualized a novel algorithm for discovering and ranking frequent itemsets in Homogeneous Multilayer Networks, achieving a 34% speedup. It was implemented using the map-reduce paradigm, employing distributed computing.

• Developed and implemented machine learning models using TensorFlow and Keras, achieving a 90% accuracyrate in predicting customer behavior, which informed targeted marketing strategies and improved conversion rates.• Conducted extensive data preprocessing and cleaning using Python libraries like Pandas and NumPy, ensuring high-quality input data for machine learning pipelines, which reduced model training time by 30%.• Designed and executed scalable data pipelines using Python, SQL, and Apache Airflow, consolidating diverse data from over 5 data sources into a centralized warehouse for real-time analytics.• Optimized data loading and transformation in Amazon Redshift, leveraging Spark for preprocessing and employing parallel loading techniques , resulting in a 47% increase in data throughput. .• Orchestrated ETL workflows using AWS Step Functions, streamlining Spark scripts execution and enabling serverless automation, resulting in a 30% reduction in processing time.• Engineered custom Kafka producers for seamless integration of streaming messages from REST applications to Kafka topics, resulting in a 60% increase in data flow efficiency.