Addisu Mulat

Data Preparation and Analysis: Extracted, transformed, and cleaned datasets from 20+ sources, conducted exploratory data analysis (EDA) which uncovered actionable insights, leading to a 30% improvement in predictive model accuracy. Data Visualization and Reporting: Created and maintained dashboards and visualizations using Power BI, Tableau, matplotlib, and seaborn, resulting in a 40% reduction in the time required for stakeholder reporting and improving data-driven decision-making speed by 25%. Machine Learning Development: Designed, implemented, and fine-tuned machine learning models using TensorFlow, scikit-learn, and PyTorch, achieving a 15% increase in model performance metrics (e.g., AUC-ROC, precision, recall) and reducing model training time by 50%. Building and Maintaining Data Pipelines: Developed and automated data pipelines for continuous data processing, resulting in a 70% reduction in data processing time and ensuring 99.9% data accuracy and integrity. Cross-Functional Collaboration: Worked with 10+ engineers, analysts, and stakeholders to define project requirements and deliver insights, contributing to a 20% increase in project efficiency and accelerating data-driven decision-making by 30%.

Engineered and maintained server-side logic, encompassing schema design and implementation for 10+ databases, while ensuring robust data integrity and security protocols that reduced data breaches by 40%. Architected and deployed RESTful APIs, integrating external services via secure endpoints and facilitating efficient communication between frontend and backend components. Optimized application performance through advanced tuning of server response times, query execution plans, and overall system throughput, resulting in a 50% decrease in response times and a 25% increase in system throughput. Designed and optimized database queries and indexing strategies, which improved data retrieval speeds by 60% and reduced query latency by 45%. Automated deployment processes using CI/CD pipelines, integrating tools like Jenkins, Docker, and Kubernetes, which reduced deployment time by 70% and minimized deployment errors by 80%.