Jonathan Narvaez

• Streamlined ETL workflows with Python and Spark, reducing actuarial processing times. • Designed dashboards to provide actionable insights for leadership. • Transitioned actuarial processes into advanced analytics workflows, fostering collaboration. • Researched data trends to enhance data quality and drive strategic decisions.

Master Thesis - Computational Approaches for Genome Scaffolding using HiChIP Data • Developed TensorFlow and PyTorch-based workflows, reducing genome scaffolding alignment time significantly. • Created CNN and LSTM algorithms, improving genome assembly accuracy. • Processed and cleaned large-scale HiChIP datasets for bioinformatics research. • Designed scalable solutions to tackle bioinformatics challenges.

• Built Python-based web crawlers to extract LinkedIn data for workforce analysis. • Analyzed datasets from top German financial firms, uncovering trends for predictive modeling. • Enhanced forecasting accuracy by 12% using integrated economic and tenure data. • Provided insights that optimized talent acquisition strategies.

• Enhanced distributed data mining systems using Hadoop (HDFS, Spark) and Linux to support AI research scalability. • Applied ML models (Clustering, Decision Trees, SVM) with MLlib, improving predictive accuracy for protein datasets. • Streamlined real-time data processing pipelines with Docker, Elasticsearch, and Kibana.

• Specialized in AI, Big Data, and cloud platforms (AWS, Azure, GCP). • Built predictive models and deep learning solutions (CNNs, RNNs) using TensorFlow and PyTorch. • Developed scalable ETL pipelines and optimized distributed systems with Spark and Kafka. • Tackled genomic and financial challenges with data-driven solutions.