AI Research@NVIDIA. Prev: Completed PhD from CSE@IITB. Passionate about Machine Learning.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
NVIDIA
Data Scientist
June 19, 2026 – Present
large-scale-ai-lectures
March 29, 2026 – Present
Lecture slides and materials on systems perspective of large-scale AI development presented at IIT Kharagpur
View Projectllm-development
July 23, 2025 – Present
This repository contains scripts and guides presented during multiple LLM development sessions by me.
View ProjectSaamayik
March 9, 2024 – October 11, 2025
Source code and dataset for the paper 'Saamayik: A Benchmark and Dataset for English-Sanskrit Translation'
View ProjectEIGEN-High-Fidelity-Extraction-Document-Images
August 21, 2023 – March 5, 2024
EIGEN-High-Fidelity-Extraction-Document-Images — GitHub repository
View Projectpe-ocr-sanskrit
June 25, 2022 – October 16, 2023
Source and Data of our EMNLP Paper 'A Benchmark and Dataset for Post-OCR text correction in Sanskrit'
View Projectrobust-aggregate-lfs
April 30, 2021 – June 5, 2022
Source code of our ACL 2022 paper 'Learning to robustly aggregate labeling functions for semi-supervised data programming'
View Projectspear
December 28, 2020 – June 27, 2024
SPEAR: Programmatically label and build training data quickly.
View Projecthyperbolic-label-emb-for-hmc
May 26, 2020 – November 3, 2021
Code for the paper Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (EACL '21)
View ProjectCultural Fit Analysis
The candidate's projects are heavily concentrated on academic research and personal contributions, primarily in NLP and ML. While this demonstrates deep technical interest, the diversity of projects outside of this specific domain is limited. The single listed professional experience at NVIDIA as a Data Scientist aligns well with the target role, but the lack of other professional experiences or diverse project types makes it challenging to fully assess broader cultural fit or adaptability to varied team environments. The candidate's experience level is listed as 0, which contradicts the current full-time role at NVIDIA, suggesting a data discrepancy or a very recent start. This impacts the assessment of cultural fit based on professional experience.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong focus on research and development, suggesting a detail-oriented and problem-solving approach. The nature of the projects implies an ability to work independently and contribute to complex technical challenges. However, without specific assessment data on communication, logical reasoning, or teamwork, it is difficult to fully assess soft skills and operational fit.