Saagarikha Srinivasan

Data Scientist

https://talent.gravityer.com/saagarikha-srinivasan

CaliforniaMember since June 27, 2026

Key Strengths

Demonstrated interest in Machine Learning and Data Science through projects like 'Automated-Bug-Triaging-to-Developer' and 'MachineLearning'.
Experience with Python and R, which are core languages for Data Scientists.
Project 'Automated-Bug-Triaging-to-Developer' shows practical application of ML for a real-world problem (bug assignment).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are primarily personal and demonstrate an interest in various technical areas including data science, information retrieval, and even game development. This indicates a broad curiosity, but the lack of team-based projects or professional experience makes it difficult to assess cultural fit for a collaborative environment. The projects align with the target role of Data Scientist, but the breadth of other projects (e.g., ChessGame, Android-Corona-Game-Engine) suggests diverse interests.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

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Projects

test-repository

May 6, 2021 – June 22, 2021

test-repository — GitHub repository

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saagarikha.github.io

November 2, 2018 – November 2, 2018

Hit here if you want to know a little bit more about what I do!!

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folder-guy

January 28, 2018 – January 28, 2018

folder-guy — GitHub repository

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ChessGame

May 16, 2017 – May 16, 2017

ChessGame — GitHub repository

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R-for-Data-Scientists

March 6, 2017 – May 5, 2017

R for data scientists.

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PycharmProjects

January 11, 2017 – January 11, 2017

OpenCV Python

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Information-Retrieval

September 5, 2016 – September 5, 2016

Information-Retrieval — GitHub repository

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Android-Corona-Game-Engine

September 5, 2016 – September 5, 2016

Android-Corona-Game-Engine — GitHub repository

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MachineLearning

September 5, 2016 – September 5, 2016

MachineLearning — GitHub repository

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Automated-Bug-Triaging-to-Developer

January 7, 2016 – January 8, 2016

For popular software systems, the number of daily submitted bug reports is high. Triaging these incoming bugs is a time consuming task. Major part of bug triaging is the assignment of a bug report to a developer with the appropriate expertise, who can resolve/fix the bug without reassigning to some other developer. We present an approach to automatically suggest developers who have the appropriate expertise for handling a bug report based on the identified software component the bug may reside in, obtained from the short description of the bug report. Our work is first to examine the impact of multiple machine learning dimensions( classifiers and training history) along with the ranked list of developers for prediction accuracy in bug assignment. We validate our approach on Eclipse Bugzilla covering 2,868,000 bug reports consisting of 253 components. We demonstrate that our techniques can achieve up to 93% prediction accuracy in bug assignment and significantly reduce the aberrant assi

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Key Strengths

Demonstrated interest in Machine Learning and Data Science through projects like 'Automated-Bug-Triaging-to-Developer' and 'MachineLearning'.
Experience with Python and R, which are core languages for Data Scientists.
Project 'Automated-Bug-Triaging-to-Developer' shows practical application of ML for a real-world problem (bug assignment).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

Saagarikha Srinivasan

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit