Location: Selden, New York
Email: hamimshabbir@gmail.com
Phone: +1-347-956-1197
LinkedIn: https://www.linkedin.com/in/hamim-shabbir-halim/
GitHub: http://github.com/Hamim-Susmit

Data Science graduate who drives data-informed decisions through analytics, machine learning, and business intelligence. Experienced in building ETL pipelines, developing predictive models, and delivering dashboards that translate complex data into actionable insights. Proven ability to analyze large datasets, optimize processes, and communicate results to diverse stakeholders.

Programming & Querying: Python (Pandas, NumPy), SQL, R
Web & Scripting: JavaScript, HTML, CSS
Data Visualization & BI: Tableau, Power BI, Excel dashboards, KPI tracking
Analytics: Data analysis, trend analysis, anomaly detection, statistical analysis
Machine Learning: Scikit-learn, PyTorch, TensorFlow
Data Engineering: ETL pipelines, Kafka, RabbitMQ
Databases: MySQL, relational modeling
Tools: GitHub, Docker, Google Cloud

• Engineered a 3D MRI tumor segmentation pipeline using PyTorch and MONAI, processing 100+ volumetric scans
• Transformed multi-modal MRI data (T1, T2, FLAIR), handling millions of 3D voxels per scan
• Implemented U-Net, nnUNet, and Swin-UNETR models, improving performance by ~12–18%
• Evaluated models using Dice Score, HD95, and ASD, achieving ~0.85+ Dice coefficient
• Optimized preprocessing pipelines, reducing training time by 20%+

• Built a BERT-based classification model achieving 95% accuracy across 50,000+ samples
• Designed ETL pipelines using Kafka and RabbitMQ, processing ~1K+ events/sec (simulated)
• Analyzed unstructured datasets, improving detection precision by ~15%
• Documented system architecture, improving reproducibility and reducing debugging time

• Developed dashboards in Tableau, Power BI, and Excel tracking 10+ KPIs
• Analyzed datasets with 10K–100K+ records to identify trends and performance gaps
• Delivered insights that improved outcomes by 15–20%
• Automated reporting workflows, reducing manual effort by ~25%

• Designed and implemented a relational MySQL database
• Optimized SQL queries, reducing execution time by 30%
• Built a Python interface to improve accessibility for non-technical users
• Developed a lightweight front-end using JavaScript, HTML, and CSS to enable dynamic data queries

• Analyzed sensor and image data to improve parking utilization
• Applied machine learning techniques to optimize system performance

Mukti Electric House — Dhaka, Bangladesh
September 2022 – December 2022

• Processed and digitized 360,000+ invoices, improving data accessibility and enabling 15% faster business operations
• Collected, organized, and analyzed sales and purchase datasets to identify trends and improve shop performance by 25%
• Automated inventory data workflows, reducing stock discrepancies by 20%
• Maintained accurate documentation of data processes to support operational transparency

Monarch Engineers — Dhaka, Bangladesh
July 2019 – March 2023

• Analyzed operational records and workflows to streamline customer and supplier data management, increasing efficiency by 30%
• Diagnosed and resolved technical issues, improving client satisfaction by 20%
• Collaborated with cross-functional teams to deliver projects with a 95% on-time completion rate

Stony Brook University — Stony Brook, NY
Master of Science in Data Science
August 2023 – December 2025 (Expected)

• Introduction to HTML5 – Coursera, University of Michigan (June 2020)
• The Non-Technical Skills of Effective Data Scientists – LinkedIn Learning (September 2024)
• Learning Excel: Data Analysis – LinkedIn Learning (October 2024)
• Learning Data Analytics: Foundations – LinkedIn Learning (October 2024)
• Learning Data Analytics Part 2 – LinkedIn Learning (October 2024)