Transforming Data Into Business Solutions
🔍 Data Detective | 🤖 ML Engineer | 📊 Insight Architect
Hi, I'm Roseline Wahome - a Data Science Strategist who turns complex datasets into clear business victories. Where others see numbers, I see narratives - hidden patterns that predict tomorrow's trends and expose today's risks.
🚀 My Data Superpowers:
- ✅ Built fraud detection systems reducing financial losses by 40%+
- ✅ Developed customer churn models with 92% prediction accuracy
- ✅ Automated data pipelines serving 1M+ daily transactions
- ✅ Designed executive dashboards adopted by 15+ Fortune 500 teams
- ✅ Trained ML models processing 10TB+ of behavioral data
- ✅ AWS-certified solutions architect for scalable analytics
My toolkit combines cutting-edge machine learning (TensorFlow, Scikit-learn) with business intuition forged through solving real-world challenges in:
🔐 Security Analytics
- Fraud Pattern Recognition
- Anomaly Detection Systems
- Blockchain Transaction Analysis
📈 Customer Intelligence
- Churn Prediction Engines
- CLTV Optimization Models
- Personalization Algorithms
When I'm not wrangling Python or optimizing neural networks, I mentor aspiring data professionals and speak about "Ethical AI in Financial Services". My work bridges the gap between technical precision and executive decision-making.
Let's Unlock Your Data Potential
Skills
Projects
🕵️♂️ Anomaly Detection: Unmasking Hidden Patterns
"In a world drowning in data, the rarest signals are often the most critical." Built machine learning models to detect outliers in high-dimensional datasets, uncovering fraudulent transactions, system failures, and operational anomalies.
📉 Customer Attrition: Predicting the Silent Exit
"Why do loyal customers leave? The answer lies in the data they leave behind." Built models to predict customer churn and created retention strategies.
📰 Named Entity Recognition
"Text is not just words—it is a goldmine of hidden entities." Built NER models to extract names, organizations, and locations from text.
💳 Credit Scoring: Decoding Trust in a Number
Redesigned credit risk models using XGBoost and SHAP explainability.
