Devin de Hueck

Data Scientist

https://talent.gravityer.com/devin-de-hueck

making stuff

New York City, New York, United States

Key Strengths

Demonstrated interest and practical experience in Machine Learning and Deep Learning through personal projects (e.g., word2vec, GANs, NEAT).
Proficiency in Python, a core language for Data Science, evidenced by multiple project implementations.
Experience with key ML/DL frameworks such as PyTorch and Jax.
Exposure to data analysis and visualization tools like Jupyter Notebook.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's personal projects show a strong initiative and self-driven learning, which can be a positive cultural fit. The diversity of projects, from core ML algorithms to web development (Java, JavaScript, HTML, CSS, Vue), indicates a broad interest, though the target role is Data Scientist. The focus on personal projects suggests a proactive approach to skill development.

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

jax-loss

January 14, 2020 – June 22, 2022

A jax library of common machine learning loss functions

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jax-skip-gram-negative-sampling

January 13, 2020 – October 20, 2024

A Jax implementation of word2vec's skip-gram model with negative sampling as described in Mikolov et al., 2013

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skip-gram-negative-sampling

August 11, 2019 – January 13, 2020

A PyTorch Implementation of the Skipgram Negative Sampling Word2Vec Model as Described in Mikolov et al.

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CS200-Fall-2019

July 28, 2019 – November 7, 2019

Github Repo for CS200 Fall 2019

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pytorch-GAN

July 2, 2019 – February 15, 2023

PyTorch implementation of the original GAN paper by Goodfellow et al.

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BostonStreetCaster

March 4, 2019 – May 12, 2019

Sidewalk image segmentation and analysis project for the City of Boston department of Public Works

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BostonCracks

November 11, 2018 – November 11, 2018

BostonCracks — repository

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pytorch-neat

July 18, 2018 – July 25, 2024

A pytorch implementation of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm

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COVE

April 11, 2018 – January 5, 2023

Software for the COVE dataset archive project

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BostonHacks2017

October 29, 2017 – October 30, 2017

BostonHacks2017 — repository

View Project

Key Strengths

Demonstrated interest and practical experience in Machine Learning and Deep Learning through personal projects (e.g., word2vec, GANs, NEAT).
Proficiency in Python, a core language for Data Science, evidenced by multiple project implementations.
Experience with key ML/DL frameworks such as PyTorch and Jax.
Exposure to data analysis and visualization tools like Jupyter Notebook.

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.

Devin de Hueck

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit