Key Strengths

Demonstrated proficiency in Python for various applications, including bot development and deep learning.
Experience with deep learning projects, specifically a quantized U-Net for real-time segmentation, indicating practical application of advanced ML concepts.
Exposure to Rust for building a neural network library from scratch, showcasing an interest in performance-oriented programming and foundational ML understanding.
Familiarity with Docker for containerization, suggesting good development practices.
Experience with Jupyter Notebooks for data science and machine learning workflows.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are predominantly personal and open-source contributions, indicating a strong drive for self-directed learning and exploration of diverse technologies (Python, Rust, TypeScript, JavaScript). The focus on deep learning, neural networks, and data-related projects (e.g., drone localization, BitUNet) aligns with a Data Scientist role. However, the lack of team-based or professional project experience makes it difficult to fully assess cultural fit in a collaborative corporate environment.

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are concise and technically focused, but do not provide insight into collaboration, problem-solving approaches, or communication style in a team setting.

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Anubhav

Data Scientist

https://www.opentalent.in/anubhav-2

19M and sleep deprived

Assam, IndiaMember since June 12, 2026

https://www.opentalent.in/anubhav-2

Top Skills

PythonDockerfileProcfileShellRustJupyter NotebooktypescriptCSSJavaScriptRouterOS ScriptHTML

Skills

PythonDockerfileProcfileShellRustJupyter NotebooktypescriptCSSJavaScriptRouterOS ScriptHTML

Projects

ferrisnet

April 11, 2026 – Present

A fully-featured automatic differentiation engine and neural network library written from scratch in Rust.

View Project

DeepLearning

March 25, 2026 – Present

DeepLearning — GitHub repository

View Project

BitUNet

March 22, 2026 – Present

A 1.58-bit ternary-quantized U-Net for real-time road scene segmentation. The model constrains all interior convolutional weights to {-1, 0, +1} using the absmean quantization scheme from BitNet b1.58, achieving a ~40 MB checkpoint size and 160+ FPS throughput on data center hardware.

drone_localization

December 15, 2025 – Present

GPS-denied drone localization using LoRa trilateration, Kalman Filter fusion, and autonomous fire detection. Built on PX4 SITL, ROS2 Humble, and Gazebo Harmonic.

View Project

tgytdl

March 27, 2022 – June 6, 2022

Telegram bot to download YouTube videos in multiple resolutions, convert video to audio

View Project

Key Strengths

Demonstrated proficiency in Python for various applications, including bot development and deep learning.
Experience with deep learning projects, specifically a quantized U-Net for real-time segmentation, indicating practical application of advanced ML concepts.
Exposure to Rust for building a neural network library from scratch, showcasing an interest in performance-oriented programming and foundational ML understanding.
Familiarity with Docker for containerization, suggesting good development practices.
Experience with Jupyter Notebooks for data science and machine learning workflows.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit