Gunjan Patait

Data Scientist

https://talent.gravityer.com/gunjan-patait

Student at IIT Roorkee.

Indore, Madhya Pradesh, India

Key Strengths

Demonstrated ability to build end-to-end ML pipelines, including model development (LightGBM), UI (Streamlit), and API integration (FastAPI) for real-time predictions.
Experience with Retrieval Augmented Generation (RAG) using LangChain and Multi-Query Retriever, indicating familiarity with advanced NLP techniques.
Practical experience with various machine learning tasks such as regression (House Prices), sentiment analysis, and exploratory data analysis (Haberman Dataset).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's personal projects demonstrate a strong interest and initiative in data science and machine learning. The diversity of projects, from traditional ML to modern NLP (RAG), suggests adaptability and a continuous learning mindset. The focus on practical applications (e.g., API for real-time predictions) aligns well with roles requiring hands-on implementation.

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are clear and concise, indicating good written communication.

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Projects

Santander-Customer-Transaction-API

April 13, 2025 – April 14, 2025

Developed an end-to-end ML pipeline for Santander Customer Transaction Prediction problem using LightGBM, with a fully functional Streamlit UI and a FastAPI backend for real-time predictions.

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House-Prices-Advanced-Regression

March 9, 2025 – March 9, 2025

This repository contains my approach to solving the Kaggle House Prices - Advanced Regression competition. The goal is to build a robust regression model that predicts house prices based on the different categorical/numerical independent house variables.

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Multi-Query-RAG-Langchain-Langsmith

February 8, 2025 – February 8, 2025

This repository demonstrates how to implement Retrieval Augmented Generation (RAG) using LangChain with a Multi-Query Retriever. The goal is to enhance retrieval by generating multiple variations of the user query, conducting similarity searches for each, and aggregating the most relevant context for answering the query.

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School_Data_Analysis

August 11, 2021 – August 11, 2021

School_Data_Analysis — GitHub repository

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Haberman_Dataset_EDA

August 8, 2020 – August 8, 2020

Haberman_Dataset_EDA — GitHub repository

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Sentiment_analysis

July 24, 2020 – July 24, 2020

Sentiment_analysis — GitHub repository

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Stocks_Price_Predictor

July 20, 2020 – July 21, 2020

Stocks_Price_Predictor — GitHub repository

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model_API

May 15, 2020 – May 15, 2020

model_API — GitHub repository

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Key Strengths

Demonstrated ability to build end-to-end ML pipelines, including model development (LightGBM), UI (Streamlit), and API integration (FastAPI) for real-time predictions.
Experience with Retrieval Augmented Generation (RAG) using LangChain and Multi-Query Retriever, indicating familiarity with advanced NLP techniques.
Practical experience with various machine learning tasks such as regression (House Prices), sentiment analysis, and exploratory data analysis (Haberman Dataset).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are clear and concise, indicating good written communication.

Gunjan Patait

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit