Professional Experience🛠️
Co-Founder & Data Scientist, Swift Traq (2024 - Present)
- Spearheaded projects, including Customer Lifetime Value Prediction and Profitability Analysis.
- Developed dashboards and reports using Excel, Power BI, SQL and Python for key stakeholders.
Projects💼
Customer Churn Prediction Dashboard
Built an ML-powered dashboard to predict customer churn using transaction history, engagement metrics, and behavioral trends. Delivered insights through:
- Predictive modeling to assess churn probability and identify at-risk customers.
- Recency, frequency, and transaction analysis for actionable retention strategies.
- Batch & manual churn predictions with dynamic visualizations for business intelligence.
Profitability Analysis
Engineered a dynamic, interactive dashboard to uncover actionable insights into business profitability. Harnessed multi-source data integration to deliver real-time metrics, including total revenue, profit margins, and order trends. Key features encompassed
- Regional profitability analysis to identify high-performing markets.
- Monthly revenue visualization for tracking performance trends.
- Top product performance insights to drive strategic decision-making.
Customer Segmentation and Analysis
Designed and deployed a cutting-edge customer segmentation dashboard, leveraging demographic, transactional, and behavioral data. Applied clustering algorithms to create actionable customer profiles, leading to enhanced targeting and retention strategies. Highlights included:
- Intuitive visualizations of customer distribution by segment.
- Segmentation-based KPIs for personalized marketing efforts.
- Actionable insights tailored for service differentiation. The tool equipped decision-makers with a robust framework to improve customer engagement and lifetime value.
Customer Lifetime Value (CLV) Dashboard
Developed an interactive stakeholder dashboard to evaluate and predict customer lifetime value, integrating sales and geographic datasets for holistic analysis. Delivered insights through:
- Metrics such as average order value, lifetime revenue, and retention trends.
- Region-specific revenue contributions to identify growth opportunities.
- Lifetime distribution analysis and top customer profiling to prioritize key accounts. This solution transformed raw data into strategic insights, enabling precise revenue maximization and informed decision-making.
Wholesale/Retail Analytics
Designed and deployed a dynamic E-commerce Insights Dashboard to analyze revenue trends, customer segmentation, cohort behavior, and geographic sales distribution. Delivered actionable insights through:
- Revenue & Sales Trends – Dynamic visualizations for tracking monthly sales performance and top-selling products.
- Cohort & Retention Analysis – Heatmap-based cohort analysis to uncover customer loyalty patterns.
- RFM Segmentation – Automated customer segmentation based on Recency, Frequency, and Monetary value.
- Peak Sales Hours Analysis – Hourly revenue trends to identify the most profitable timeframes.
Education🎓
WorldQuant University
Applied Data Science Lab (2025 - )
University of Eldoret
Bsc. Computer Science (2013 - 2017)