Nihar Dwivedi
Data Scientistengineering @sifive
Sifive
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
engineering @sifive
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Sifive
Data Scientist
June 16, 2026 – Present
image-captioning
December 6, 2020 – December 11, 2020
Project implementing state-of-the-art deep learning models for Image Captioning - generating a descriptive caption for an image.
View ProjectN-body-simulation-using-Barnes-Hut
May 4, 2020 – May 5, 2020
N-body-simulation-using-Barnes-Hut — GitHub repository
View ProjectData-Science-and-Openshift-on-the-MOC
January 27, 2020 – October 22, 2020
Project implementing new ML model for a containerized ML Redhat app running on Openshift on the Mass Open Cloud.
View Projectmobile-machine-learning
November 26, 2019 – March 3, 2021
A project exploring modern embedded machine learning frameworks on modern mobile platforms.
View Projectminiproject2
October 15, 2019 – October 23, 2019
Mini Project 2 Repo for the course EC601, Product Design in ECE at BU.
View ProjectSteel-Defect-Challenge
September 18, 2019 – November 11, 2019
A Kaggle data science challenge submission containing my team's entry.
View ProjectWorkingTitle
September 11, 2019 – August 6, 2020
A set of python scripts to scrape Twitter for tweets containing a particular keyword and generate sentiment scores.
View Projectneural-hack
March 9, 2017 – March 10, 2017
solutions to the neural hack competition organized at Luminaire, KIIT University.
View ProjectCultural Fit Analysis
The candidate's projects show a strong inclination towards data science and machine learning, aligning with a Data Scientist role. The diversity of projects, from Kaggle competitions to deep learning implementations and mobile ML, indicates a proactive and curious individual. However, the lack of detailed descriptions for some projects and the absence of team-based project descriptions make it difficult to fully assess collaboration and broader cultural fit.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. No psychometric test results or interview feedback provided.