Simanta Barman

Data Scientist

https://talent.gravityer.com/simanta-barman

University of Minnesota

Key Strengths

Strong foundational understanding of Python for data science and machine learning, evidenced by projects like 'Non-Linear-Optimization' and 'Custom-Matrix-and-Neural-Net' which involve implementing algorithms from scratch.
Experience with various machine learning algorithms (SVM, Linear Regression, Logistic Regression, Perceptron, LSTM) and their practical application.
Demonstrated interest in traffic assignment algorithms and optimization, which aligns with quantitative problem-solving.
Exposure to Rust for performance-critical applications, indicating a willingness to explore diverse programming paradigms.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's project portfolio shows a strong inclination towards individual, self-directed learning and implementation of core algorithms. The current role as 'Data Scientist' at the University of Minnesota, though recent, suggests alignment with the target role. However, the lack of team-based projects or contributions to larger open-source initiatives makes it difficult to assess collaborative cultural fit. The diversity of projects (Python, Java, Rust) indicates adaptability and a broad technical curiosity.

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

informs2025

October 24, 2025 – October 24, 2025

informs2025 — GitHub repository

View Project

Game-of-life-rust

August 12, 2025 – August 12, 2025

Three Rust implementations of Conway’s Game of Life: a terminal version, a minifb+HashSet version for efficient alive-cell checks, and a tch (libtorch CPU) tensor-based version for massive grids up to 5000×5000. Explore performance trade-offs, data structures, and real-time visualization.

View Project

pathf-lstm

February 7, 2024 – March 26, 2024

working code for lstm (using CBS validation)

View Project

Custom-Matrix-and-Neural-Net

December 22, 2023 – December 22, 2023

Contains an implementation of a Linear Algebra and a Neural Network library from scratch in pure python without any external dependencies. The syntax for the Linear Algebra library is created to be similar to numpy.

View Project

traffic-algorithms

November 4, 2022 – November 4, 2022

Contains mostly static traffic assignment algorithms

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Max-Pressure-Controls

September 11, 2022 – September 11, 2022

Max-Pressure-Controls — GitHub repository

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traffic_network_creater

September 4, 2022 – September 4, 2022

traffic_network_creater — GitHub repository

View Project

Non-Linear-Optimization

May 15, 2022 – August 12, 2022

Contains SVM, Linear Regression, Logistic Regression and Perceptron from scratch

View Project

BLU_stuff

June 19, 2021 – June 19, 2021

BLU_stuff — GitHub repository

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AVDTA

May 21, 2017 – February 2, 2024

AVDTA — GitHub repository

View Project

Key Strengths

Strong foundational understanding of Python for data science and machine learning, evidenced by projects like 'Non-Linear-Optimization' and 'Custom-Matrix-and-Neural-Net' which involve implementing algorithms from scratch.
Experience with various machine learning algorithms (SVM, Linear Regression, Logistic Regression, Perceptron, LSTM) and their practical application.
Demonstrated interest in traffic assignment algorithms and optimization, which aligns with quantitative problem-solving.
Exposure to Rust for performance-critical applications, indicating a willingness to explore diverse programming paradigms.

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.

Simanta Barman

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Experience

Projects

Key Strengths

Cultural & Operational Fit