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"In God we trust. All others must bring data." — W. Edwards Deming
I'm a passionate Data Scientist who transforms raw data into actionable business insights and intelligent AI systems. My expertise spans the entire data science pipeline from data collection and preprocessing to model deployment and monitoring.
- 🔭 Working On: Advanced time series forecasting models and real-time analytics dashboards
- 🌱 Learning: Deep reinforcement learning, MLOps best practices, and edge AI deployment
- 🛠️ Building: End-to-end ML pipelines with automated model retraining and A/B testing
- 💬 Ask Me About: Statistical modeling, feature engineering, model interpretability, data visualization
- ⚡ Beyond Data: Piano player 🎹 who finds patterns in music like algorithms in data 🎨
📊 Core Data Science Stack
| Category | Technologies |
|---|---|
| Languages |     |
| Data Manipulation |     |
| Machine Learning |     |
| Deep Learning |     |
| Visualization |     |
| Statistical Analysis |     |
🚀 MLOps & Production Stack
| Category | Technologies |
|---|---|
| MLOps Platforms |     |
| Feature Stores |    |
| Model Serving |     |
| Orchestration |     |
| Cloud & Infrastructure |     |
📊 Big Data & Analytics
| Category | Technologies |
|---|---|
| Big Data Processing |     |
| Data Warehouses |     |
| Databases |     |
| BI & Dashboards |     |
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- Advanced Feature Engineering Techniques for Time Series
- Building Production-Ready ML Pipelines with MLflow and Airflow
- Interpreting Black-Box Models: A Practical Guide to SHAP and LIME
- Real-time Model Monitoring and Drift Detection
| Research Area | Current Project/Learning |
|---|---|
| Deep Learning | Transformer architectures for time series forecasting |
| MLOps | Implementing continuous training pipelines with Kubeflow |
| Causal AI | Causal inference methods for business decision making |
| Edge AI | Deploying lightweight models on mobile and IoT devices |
| Explainable AI | Advanced model interpretability for regulated industries |
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Advanced ensemble models predicting CLV with 94% accuracy using XGBoost and neural networks |
Streaming analytics platform detecting anomalies in IoT sensor data using autoencoders |
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Production-ready sentiment analysis using BERT with FastAPI serving 1000+ requests/min |
Reinforcement learning agent optimizing pricing strategies with 15% revenue increase |
| Metric | Target | Current |
|---|---|---|
| 📈 Models Trained | 2/day | 0 |
| 📊 Datasets Explored | 1/day | 0 |
| 🧠 Papers Read | 3/week | 0 |
| 💻 Code Commits | 5/day | 0 |
| 📝 Blog Posts | 2/week | 0 |
Always excited to discuss ML algorithms, share insights on data patterns, or collaborate on predictive modeling projects. Whether you want to explore neural architectures, debate feature engineering strategies, or build production ML systems together, let's connect!
Currently in DATA SCIENCE RESET MODE 📊🚀 - Immersed in algorithms, models, and insights!
"Data is the new oil, but analytics is the combustion engine." — Peter Sondergaard