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C-DAC R&D
Data Scientist
June 18, 2026 – Present
biRNN-Sentiment-Analysis
May 19, 2023 – May 19, 2023
This repository contains the concepts and implementation code of Bi-directional LSTM. Which help to learn the sequential data pattern from both direction i.e forward and backward.
View ProjectLSTM-Neural-Network-for-Time-Series-Prediction
May 15, 2023 – May 15, 2023
LSTM-Neural-Network-for-Time-Series-Prediction — GitHub repository
View ProjectTrack-And-Count-Object-using-YOLO
May 10, 2023 – May 10, 2023
Advanced Vehicle Tracking and Detection System using ByteTrack, Supervision, and YOLO Algorithms
View ProjectDetect-and-count-objects-in-polygon-zone
May 9, 2023 – May 10, 2023
Person detected and counted zone-wise
View Projectneural-compressor
April 12, 2023 – April 12, 2023
Intel® Neural Compressor (formerly known as Intel® Low Precision Optimization Tool)
View ProjectMovie-Recommender-System-Using-Machine-Learning
March 13, 2023 – March 13, 2023
Movie-Recommender-System-Using-Machine-Learning — GitHub repository
View Projectcircleci-pipeline-template
December 4, 2022 – December 4, 2022
This repository is the template for the circleci pipeline with GCP machine
View Projectfinance-complaint
December 3, 2022 – December 3, 2022
finance-complaint — GitHub repository
View ProjectObject_detector_and_object_identification
November 21, 2022 – November 21, 2022
This is the sample assignment of first detect the object and then recognise or identify the object from same class
View ProjectCultural Fit Analysis
The candidate demonstrates a strong initiative through numerous personal projects, indicating a proactive and self-driven learning attitude. The diversity of projects, from object detection to time series prediction and recommender systems, suggests adaptability and a broad interest within the Data Scientist domain. The current role as a Data Scientist at C-DAC R&D aligns well with the target role, indicating a clear career path and relevant experience.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are concise, but there is no information on communication clarity, teamwork, or problem-solving approaches in a collaborative environment.