Jackline Ngenia

• Utilizes data analysis techniques to score and set lending limits for small businesses, contributing to more accurate risk assessment and lending strategies
• Spearheads data cleaning initiatives, ensuring the accuracy and integrity of data used for analysis and decision-making
• Creates comprehensive Tableau reports for data visualization, aiding in easy comprehension and decision-making for stakeholders
• Transforms complex data sets into insightful visualizations that assist in identifying trends, patterns, and areas for optimization
• Collaborates closely with cross-functional teams, including colleagues from various departments, to gather insights, share findings, and contribute to overall business objectives
• Develops innovative solutions to challenges related to data quality, model accuracy, and process efficiency, resulting in streamlined workflows and improved outcomes
• Contributes to the company's bottom line by providing data-driven insights that inform strategic decisions, leading to optimized lending strategies and improved business performance.

• Assisted in creating visually appealing dashboards that effectively communicated insights to stakeholders
• Supported data cleaning efforts by identifying and rectifying inconsistencies, contributing to improved data quality
• Gained experience in data preprocessing techniques, ensuring accurate and reliable data for analysis
• Worked closely with the Data Science team, contributing to data projects and sharing insights during team discussions
• Absorbed knowledge rapidly and demonstrated a proactive attitude towards learning new tools and techniques.

• Project name: Facilitating access to financial services for underserved populations in Africa
• I collaborated in the Omdena AI with the impact startup Toju Africa within 8 weeks with 30+ change makers from all over the world to build an AI solution

Achievements

• Worked on the loan capacity prediction for the MSMEs given their different features
• Successfully performed preprocessing and analysis on the data
• Implemented models to predict minimum and the maximum loan amount, and the likelihood of loan default
• Deployed the models on a dashboard.