DERARTU WERJI

• Analyzed data to boost operational efficiency, aiding executive decision-making. Deliver timely, quality analysis, interpretation, and synthesis of data into effective, concise, and actionable recommendations.
• Developed SQL, R, and Python solutions for impactful data insights, coupled with big data architecture and pipeline, Hadoop, Hive, and Spark.
• Collaborated with IT to enhance data processes, improving report accuracy.
• Implemented data mining and machine learning for actionable insights.
• Created data visualizations to transform raw data into stakeholder insights.
• Applied advanced statistical methods to uncover hidden patterns in large datasets, enabling data-driven strategic planning and operational improvements.
• Designed and implemented machine learning models for customer behavior analysis, leading to substantial improvements in predictive accuracy.
• Orchestrated data pipeline optimization projects with IT teams, resulting in faster processing times and enhanced data reliability.
• Pioneered automated reporting solutions using Python, transforming complex data into actionable insights for business stakeholders.
• Refined data quality protocols and validation procedures, establishing robust standards for analytical reporting and decision support.
• Engineered scalable machine learning solutions for real-time customer behavior analysis, enhancing model accuracy, and deployment efficiency.

key Accomplishments

Healthcare Patient Segmentation: Segmented patients based on health conditions and demographics using K-Means clustering. Improved resource allocation for preventive care by 22% in a simulated healthcare dataset. Analyzed over 500K records from a publicly available Kaggle EHR dataset. Developed a predictive model to flag high-risk patients for chronic diseases.

Key Tools: Python, Scikit-Learn, Matplotlib.

Market Direction Predictor:

Technologies: Python, Pandas, NumPy, Scikit-Learn, Matplotlib.

Description: Developed a predictive analytics model as part of a course project aimed at forecasting market trends. Utilized statistical analysis and machine learning algorithms to analyze historical market data and predict future directions.

Achievements: Achieved model accuracy of over 85%, outperforming baseline predictions by 15%. Enhanced personal expertise in quantitative finance and machine learning applications in economic forecasting.