Hey there! Iβm Neha, a Data & ML Specialist who finds joy in uncovering stories hidden within data. β¨π·
Iβve spent 3+ years designing data pipelines, building ML models, and transforming raw data into meaningful insights. I recently earned my Masterβs in Applied Data Science from San JosΓ© State University, where I also served as a Graduate Teaching Assistant for Machine Learning and Distributed Systems.
When Iβm not coding or analyzing trends, Iβm probably dancing to my favorite tunes or training for my next run - because balance is everything. πβπ»
π Iβm currently exploring Explainable AI and Foundation Models
π― Iβm looking to collaborate on ML/AI projects that solve real-world problems
π¬ Ask me about Data Engineering, ETL Pipelines, Machine Learning, Deep Learning
π MS Applied Data Science @ SJSU
πΌ Ex-DXC Technology Data Engineer
β‘ Fun fact: Iβve analyzed everything from fashion trends to Higgs Boson particles!
LLM Chain-of-Thought ReasoningDeveloped explainable reasoning approaches for educational problem-solving using LLMs. Built end-to-end language model pipeline. |
Code Generation LLM AnalysisExplainability-driven evaluation framework for code-generating LLMs. Analyzed 10 models with Python, uncovering actionable insights through execution testing. |
AWS ETL Weather Data PipelineBuilt end-to-end ETL pipeline ingesting data from NOAA GHCN-D AWS S3. Processed 10GB+ data with scheduled jobs, queries, and visualization. |
Global Food Insecurity AnalysisDeveloped automated ETL pipelines analyzing global food insecurity patterns. Applied K-means clustering and regression across 217 countries. |
Object Detection with CNNsDeveloped and compared multiple object detection models including YOLO, Faster R-CNN, and Detectron2. Handled real-world images and videos using PyTorch and OpenCV. |
Generative Models for FashionDesigned and trained GAN and LSGAN using Fashion-MNIST. Explored Wasserstein GAN variant to generate realistic fashion images with improved visual quality. |
Wide & Deep Recommender SystemEngineered features and optimized training on 10GB+ data with PyTorch for personalized recommendations. Implemented hybrid model architecture. |
Duplicate Question DetectionDeveloped NLTK and TensorFlow-based detection system for semantically similar questions. Improved accuracy by 5% through novel graph-based features. |
Higgs Boson DetectionUtilized Apache Spark to analyze 8GB ROM data from high-energy collision experiments. Achieved 55% predictive accuracy using XGBoost hyperparameter optimization. |
VTA Ridership ForecastingIntegrated VTA ridership and NOAA weather data using Pandas and Airflow. Trained Random Forest models achieving 55% accuracy and identified weather impact. |
π Master of Science in Applied Data Science (Data Analytics)
San Jose State University | Aug 2023 - May 2025
π Bachelor of Engineering in Information Technology
Jawaharlal Nehru Technological University | 2014 - 2018
π Certifications:
| π¬ Graduate Teaching Assistant | San Jose State University |
| Machine Learning Technologies & Distributed Systems | Aug 2024 - May 2025 |
| π Data Engineer | DXC Technology |
| Built scalable ETL pipelines and ML solutions | Aug 2018 - Jan 2022 |
Iβm always excited to collaborate on innovative ML/AI projects, discuss data science trends, or explore opportunities that create real-world impact. Feel free to reach out!