MSc Data Analytics @ BSBI Berlin | Data Scientist | ML Engineer
Architecting Scalable Forecasting Engine & Industrial Computer Vision
Actively Interviewing: Seeking Full-Time Data Science / ML Engineer roles.
Location: Hyderabad, India (Available for Local & GCC Roles) | Globally Mobile.
Availability: Immediate Start.
- Big Data Architect: Engineered a 15.2M record pipeline using PySpark and GCP Dataproc.
- Memory Optimization: Achieved 70% RAM reduction via advanced data downcasting and feature engineering.
- Computer Vision: Developed an industrial-grade ReUNet model achieving 91.7% Accuracy.
- Technical Leadership: Former Founder & Lead Developer at CodeMacrocosm; scaled an open-source community to 2,100+ members and 1.2k+ Stars.
| Category | Tools & Technologies |
|---|---|
| Machine Learning | Python, Scikit-Learn, LightGBM, XGBoost, TensorFlow, OpenCV |
| Data Engineering | PySpark, GCP (Dataproc/BigQuery), SQL (PostgreSQL), ETL Pipelines |
| Statistical Research | Predictive Modeling, Time-Series Forecasting, Sales Analytics |
| Software Ops | Git/GitHub, DSA (O(n) Optimization), Docker, Flask API |
Scale: 15.2 Million Transactions | Tech: PySpark, LightGBM, GCP
- The Problem: High-latency and memory crashes during large-scale retail demand forecasting.
- The Solution: Implemented a Tweedie-loss LightGBM model with a memory-optimized data loader.
- The Result: 70% less memory usage and 15% higher accuracy than baseline models.
- The Description: A Strategic HR Intelligence Workspace leveraging Behavioral Analytics to predict employee attrition. Features a flight-risk engine analyzing Retention Rates, Income-to-Attrition correlation, and patterns.
- Tech Stack: Python, Advanced SQL, Data Modeling
- The Description: A high-precision Computer Vision workspace featuring ReUNet for medical segmentation and MobileNetV2 for AgTech classification. Demonstrating cross-domain AI expertise in Healthcare Diagnostics.
- Tech Stack: PyTorch, Jupyter Notebook, OpenCV
🔍 Click to View Additional AI, Big Data & Analytics Projects
- Retail-Sales-Intelligence-Dashboard
- Impact: Architected a multi-branch retail performance dashboard tracking Total Sales, Gross Income, and Tax (5%) across diverse product categories and cities.
- Amazon-BigData-Verified-Review-Classifier
- Impact: Scalable Trust-Signal Detection: A Big Data pipeline using PySpark and GCP Dataproc to classify 8GB+ of Amazon reviews with high-precision Random Forest modeling. Engineered for horizontal scalability using Hive bucket partitioning.
- Retail-Data-Engineering-Pipeline
- Impact: Scalable ETL Pipeline: Processing 5M+ retail records with PySpark on GCP Dataproc. Automated the extraction of global business KPIs and consumer trends. Includes an Ethical Data Framework.
- Biometric-Attendance-Engine
- Impact: Real-time face recognition system using HOG encodings and Dlib landmarks. Features a high-speed Flask/OpenCV pipeline for live video processing and automated SQL database logging.
- Intelligent-Travel-Recommendation-Engine
- Impact: An Intelligent Travel Recommendation Engine using TF-IDF Vectorization and KNN to predict optimal tourist destinations. Features a modular Python/Tkinter architecture.
- Mushroom-Classification-Predictive-Analytics
- Impact: High-precision predictive classification achieving 100% accuracy using Random Forest & XGBoost. Optimized via GridSearchCV to ensure zero-false-negative outcomes in safety-critical settings.
- The Description: A centralized showcase highlighting my versatility across Software Development, Tech Leadership/Direction, and Creative Engineering projects. It acts as a curated window into my most impactful and multifaceted work.
I write Production-Grade Python. I focus on
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