James Hazelden

Data Scientist

https://talent.gravityer.com/james-hazelden

PhD student at University of Washington. Passionate about graphics, simulation, and neuro-AI

Seattle, Washington, United States

Key Strengths

Demonstrated interest in machine learning, neural networks, and gradient descent through multiple personal projects.
Proficiency in Python, a core language for Data Science.
Experience with Jupyter Notebooks for data analysis and model development.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are primarily research-oriented and academic, focusing on the theoretical aspects of neural networks and computational models. While this demonstrates a strong intellectual curiosity, the lack of team-based projects or industry-specific applications makes it difficult to fully assess cultural fit for a typical industry Data Scientist role. The projects align with the technical aspects of a Data Scientist role, particularly in research-heavy environments.

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

lift-backed

March 30, 2026 – Present

Backend flask code for a page on my website

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Rich_Lazy_FTLE

January 20, 2026 – Present

Rich_Lazy_FTLE — GitHub repository

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agviz

October 17, 2025 – October 18, 2025

AutoGrad Flow (agflow): Visualize flow of states/parameter flow in autograd tree for neural networks and similar models

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kpflow

July 14, 2025 – Present

Analysis of gradient descent learning in general recurrent models using operators

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adjoint_dynamics

April 16, 2024 – May 15, 2025

Studying Features of Neural Networks (Discrete and Continuous Time) Through the Backwards Flow Dynamics

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smoothGrad

May 17, 2023 – May 17, 2023

Code for smooth gradients paper Training Networks Consisting of Biophysical Neuron Models Through Smooth Monte-Carlo Gradient Estimation

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neuro_ai_lab

February 10, 2023 – May 13, 2023

Aimed to be a collection of different code I accumulate for training of neuroscience related models or benchmarks.

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knc-neural-calculus.github.io

September 8, 2020 – January 22, 2023

knc-neural-calculus.github.io — GitHub repository

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GLS

July 29, 2018 – August 9, 2018

Generalized Lindenmayer System (GLS): Implements L-Systems parser with hooks into code, parameters, basic expressions, etc.

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Isosurface-Renderer

March 3, 2017 – July 31, 2018

Isosurface renderer using marching cubes. Written for Modern OpenGL (specifically 4.5) with GLFW 3 and GLM.

View Project

Key Strengths

Demonstrated interest in machine learning, neural networks, and gradient descent through multiple personal projects.
Proficiency in Python, a core language for Data Science.
Experience with Jupyter Notebooks for data analysis and model development.

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.

James Hazelden

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit