Sagnik Roy
Data ScientistHumble about growth!
JGEC
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Humble about growth!
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
JGEC
Data Scientist
June 20, 2026 – Present
Tabular-AutoML
January 13, 2022 – February 19, 2023
Python Auto-ML Package for Tabular Datasets
View ProjectML-Gallery
September 29, 2021 – October 29, 2021
A Special Open-Source Repository for all kinds of Data Science and Machine Learning Projects! Contribute your EDA Case Studies, ML Models, Deep Learning Algo, and fun Computer Vision ideas!
View ProjectRhythm-Finder
September 29, 2021 – February 1, 2024
ML-powered Music Recommendation Engine
View ProjectDeep-Learning-Competitions
September 14, 2021 – March 14, 2022
Solutions of Deep Learning Competitions
View ProjectFractional-Gabor-Convolutional-Network
August 24, 2021 – September 17, 2021
Pytorch Implementation of FGCN
View ProjectU-Net-Reduced-with-TF-keras
June 14, 2021 – September 21, 2021
Complete U-net Implementation with keras
View ProjectLoan-Web
April 29, 2021 – September 13, 2021
ML-powered Loan-Marketer Customer Filtering Engine
View ProjectCultural Fit Analysis
The candidate shows a strong passion for data science and machine learning through a diverse portfolio of personal projects. The current role as a Data Scientist at JGEC aligns well with the target role. However, the lack of team-based projects or contributions to larger open-source initiatives (beyond ML-Gallery description) makes it difficult to fully assess collaborative cultural fit. The experience level is listed as 0, which contradicts the current full-time role, suggesting potential data inconsistency or a very recent start.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's profile primarily highlights technical project work without details on collaboration, problem-solving approaches, or communication styles.