Keerthan S S

Data Scientist

https://talent.gravityer.com/keerthan-s-s

Bengaluru, Karnataka, IndiaMember since June 16, 2026

Key Strengths

Strong foundational knowledge in Reinforcement Learning (RL) demonstrated through multiple personal projects (PPO, Action-Constraints-RL, Multi-Agent-Snake, Checkmate-with-Rook).
Proficiency in Python, a core language for Data Science and Machine Learning.
Experience with various machine learning concepts, including deep learning (Deep-Q learning) and user interest prediction.
Demonstrated ability to work on complex, self-directed projects (e.g., C-compiler, Synthesia-to-Sheet).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are heavily concentrated in Reinforcement Learning and general Machine Learning, which aligns well with a Data Scientist role. However, the lack of diverse project types outside of ML/RL and the absence of team-based or professional experience might indicate a narrower scope of collaboration experience. The projects are all personal, which suggests self-motivation but provides no insight into team dynamics or corporate environment fit.

Soft Skills & Operational Fit

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

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Projects

cmpe257-ml-wavelets-to-weights

May 1, 2026 – Present

cmpe257-ml-wavelets-to-weights — GitHub repository

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PPO

April 28, 2022 – April 28, 2022

An implementation of PPO in the discrete and continuous domains

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Learning-Costs-and-Rewards

April 5, 2021 – August 11, 2021

Learning-Costs-and-Rewards — GitHub repository

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Action-Constraints-RL

March 22, 2021 – March 22, 2021

This code accompanies the paper "Reinforcement Learning with Action Constraints"

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Checkmate-with-Rook

November 17, 2019 – March 16, 2020

This repository is for using DRL to checkmate a king with the help of a rook within 50 moves.

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Multi-Agent-Snake

April 7, 2019 – June 2, 2019

Implemented Actor-Critic and Asynchronous Deep-Q learning algorithms for multi-agent snake game, wherein the agents compete with each other to maximise their score.

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Synthesia-to-Sheet

January 14, 2019 – May 3, 2019

This project aims to address the woe of many pianists, by providing a hassle-free easy conversion tool from synthesia videos on youtube into sheet music.

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User-interest-prediction

November 10, 2017 – November 13, 2017

Discover a user's interests based on search history

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C-compiler

March 10, 2017 – March 31, 2017

A mini compiler for a subset of the C programming language.

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Cesura

March 22, 2016 – April 14, 2016

Cesura is a music discovery Android app. Apart from being a normal music player, the app learns the user's interests and recommends relevant songs

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Key Strengths

Strong foundational knowledge in Reinforcement Learning (RL) demonstrated through multiple personal projects (PPO, Action-Constraints-RL, Multi-Agent-Snake, Checkmate-with-Rook).
Proficiency in Python, a core language for Data Science and Machine Learning.
Experience with various machine learning concepts, including deep learning (Deep-Q learning) and user interest prediction.
Demonstrated ability to work on complex, self-directed projects (e.g., C-compiler, Synthesia-to-Sheet).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

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

Keerthan S S

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit