Associate Data Scientist with 2+ years of experience in `end-to-end` ML lifecycle management, from exploratory analysis to production deployment.
- ✔️ Core Expertise: Predictive Modeling (Regression/Classification), NLP, Computer Vision, MLOps
- ✔️ Proven Impact: Built and deployed 10+ ML models improving business KPIs by 15-30%
- ✔️ Certifications: Post Graduate Program in Data science and Analytics
| Data Engineering | ML/DL | Cloud & Tools | Visualization |
|---|---|---|---|
| SQL • Spark • Airflow | Scikit-learn • XGBoost • LightGBM | AWS Sagemaker • Azure ML | Power BI • Tableau |
| Python • PySpark | TensorFlow • PyTorch • Keras | MLflow • Docker • Kubeflow | Matplotlib • Seaborn |
| PostgreSQL • MongoDB | OpenCV • Transformers (HuggingFace) | Git • GitHub Actions | Plotly • Streamlit |
- Automated Fraud Detection: Reduced false positives by 22% using ensemble models (XGBoost + LightGBM) for a fintech client.
- NLP Pipeline Optimization: Deployed BERT-based text classification reducing inference time by 40% via ONNX runtime.
- MLOps Implementation: Designed CI/CD pipelines with GitHub Actions + MLflow, improving deployment frequency by 3x.
- Computer Vision Solution: Built YOLOv8-based defect detection system achieving 98.5% accuracy in manufacturing QA.
You’ll find a variety of regression, classification, NLP tasks, Deep Learning in my GitHub repositories.
💡 Check out my repos:
I'm open to:
- ✅ ML Engineering roles
- ✅ End-to-end data product development
- ✅ Open-source contributions in AI/ML
- ✅ Technical writing partnerships
📫 Reach me at 132anaskhan@gmail.com 🔗 Connect on LinkedIn or explore my work on Kaggle.
Feel free to explore my repositories, contribute, or just say hi! 😊