Mandeep Singh Profile Picture

Hello, I'm

Mandeep Singh

Data Scientist

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About Me

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Data Science Intern

Rally Vision
Sept-Dec 2024

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M.Tech (Computation and Data Science)

IISc Bangalore
8.4/10 CGPA

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B.E. (Chemical Engineering)

BITS Pilani KK Birla Goa Campus
8.03/10 CGPA

I'm a Data Scientist with a background in Chemical Engineering and an M.Tech in Computational and Data Science from IISc Bangalore.
My expertise lies in building end to end Machine Learning systems, from custom feature engineering and model development to containerizer deployment on cloud infrastructure.
Interested in applying AI to solve real-world problems and creating impactful solutions that are not just accurate, but scalable and production ready.

What I Work With

Technical Skills

Languages

Python SQL

Machine Learning & Data Science

Scikit-learn Pandas NumPy

Deep Learning

PyTorch TensorFlow (Keras) Transformers

Natural Language Processing

Hugging Face TF-IDF Text Embeddings

Audio & Signal Processing

Librosa YAMNet Embeddings

Deployment & MLOps

FastAPI Docker ONNX AWS EC2

Web Scraping & Retrieval

Scrapy Beautiful Soup Chroma DB OpenAI API

Tools

Git

Browse My Recent

Projects

Fungi Image Classification

Built an end-to-end image classification system using EfficientNetV2 with selective fine-tuning of deeper layers and a custom classifier head.
Optimized deployment by exporting the model to ONNX for fast inference on a resource-constrained AWS EC2 instance.

Movie Reviews Sentiment Analysis

Built an end-to-end sentiment analysis system using both a fine-tuned DistilBERT model and classical ML approaches.
Deployed a TF-IDF + Logistic Regression model on AWS for fast, low-latency inference.

Music Genre Classification

Built a music genre classification system using both audio features and YAMNet embeddings.
Compared multiple ML models and deployed an optimized Logistic Regression pipeline for real time inference.

CUDA Runtime API RAG Assistant

Built a domain specific question answering system for NVIDIA CUDA Runtime API documentation using Retrieval Augmented Generation (RAG).
Implemented hybrid retrieval (dense embeddings + BM25), Reciprocal Rank Fusion (RRF), and Cohere reranking to improve retrieval quality.

Get in Touch

Contact Me