Data Scientist & ML Engineer — M.S. Data Science, UT Arlington (2025) · Dubai, UAE
I build end-to-end data pipelines, ML models, and computer vision systems. Currently an ML Engineer Intern at Dezzex Technologies, working on real-time object detection for live CCTV video analytics pipelines using YOLO-based architectures.
- Data Engineering — end-to-end pipelines, data quality contracts (Great Expectations, PyDeequ/AWS Deequ), CI/CD automation
- Machine Learning — predictive modeling, consumer segmentation, time-series forecasting (95% accuracy on ERCOT grid data)
- Computer Vision — YOLOv8 object detection, real-time video analytics, image detection for surveillance pipelines
- Analytics & BI — Power BI, Tableau, Looker dashboards; reduced stakeholder insight time by 30% at Cardinality AI
- Cloud & Scale — AWS (Glue, S3, EMR), GCP (BigQuery, Dataflow), PySpark on billion-row datasets
Languages
ML & AI
Data Engineering
Cloud & Infra
Visualization & BI
Medicare GX Data Contracts · Python Great Expectations PostgreSQL Docker GitHub Actions
Data quality contracts on the CMS Medicare 2023 dataset (10M+ provider records). 20+ typed expectations across 7 quality dimensions. CI/CD pipeline auto-generates browsable GX Data Docs HTML reports on every push.
Data Quality Validation Pipeline · PySpark PyDeequ AWS Deequ Python
Automated validation pipeline on 3–4M rows/month of NYC Yellow Taxi records. 12 constraint checks, column profiling, drift detection. Documented AWS Glue migration path for billion-row scale.
ERCOT Grid Analytics Dashboard · Python Tableau Time-Series Analysis
Aggregated 10+ years of ERCOT API data (~100K data points). Time-series models achieved 95% forecast accuracy on energy production and grid stability patterns.