Jonathan Sokoll
Head Of AI Developer Relations & Strategy
Memphis, Tennessee, United States
Summary
👤
Senior🎓
Top SchoolJonathan Sokoll is a Head of AI Developer Relations & Strategy at Lockheed Martin with 11 years of experience building AI communities, designing enterprise AI/ML talent programs, and translating research into operational impact. He blends classroom-tested teaching—over 1,200 hours of machine learning instruction—and hands-on data science work (notably NLP and RNN/LSTM experimentation) with a background in management consulting and public-sector analytics. Jonathan has led data-driven consulting engagements at Deloitte, architected AI solutions, and now focuses on advocacy, developer enablement, and scalable workforce development within a large defense contractor. Based in Memphis, he pairs an international studies and economics BA with early economics research and Python experience, which gives him an uncommon mix of policy-minded systems thinking and practical ML engineering.
11 years of coding experience
13 years of employment as a software developer
BA International Studies and Economics, BA International Studies and Economics at Rhodes College2010 Maryville High School
English, French
Stackoverflow
71reputation
18kreached
1answer
0questions
Github Skills (18)
tokenize10
python10
machine-learning10
rnn-model10
n10
keras10
tokenizer10
lstm10
tensorflow10
natural-language-processing10
backpropagation10
neural-network10
jupyter-notebook10
nlp10
notebook9
Programming languages (2)
Github contributions (5)
bloominstituteoftechnology/DS-Unit-4-Sprint-3-Deep-Learning
May 2019 - Sep 2020
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 56 commits, 145 PRs in 1 year 4 months
Contributions summary:Jonathan's commits focus on modifications to an IPython Notebook file (`.ipynb`) related to Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM). These changes include incorporating code for loading and utilizing text data from Wikipedia articles, suggesting a focus on natural language processing tasks. The code adjustments strongly suggest a hands-on approach to experimenting with and potentially training RNN/LSTM models for text-based applications.
bloominstituteoftechnology/DS-Unit-4-Sprint-1-NLP
Apr 2019 - Sep 2020
Role in this project:Data Scientist
Data ScientistContributions:48 commits, 188 PRs, 46 pushes in 1 year 4 months
Contributions summary:Jonathan's commits focus on modifications within a Jupyter Notebook related to Natural Language Processing (NLP). The code changes indicate an introductory exploration of text data and tokenization, including the import of necessary libraries and potential setup for further NLP tasks. The commit messages reflect experimentation with code changes.
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