Jared Ostmeyer

Principally A Data Scientist

Dallas, Texas, United States
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Summary

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Rockstar
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Top School
Jared Ostmeyer is a data scientist and applied machine learning researcher with 11 years of experience developing deep learning solutions for biomedical problems, from using an iPhone camera to assess insulin readiness to integrating 3D molecular structures with sequence-trained networks. He has translated academic inventions into product-focused programs—authoring patents, securing grants, and building active learning pipelines and efficient binding predictors used alongside experimental teams. Jared’s background spans academia and industry, including roles as assistant professor, senior scientist at Amgen, and AI lead at Ordaos Bio, where he scaled models across distributed GPUs and piloted federated learning on sensitive patient data. He contributes open-source ML examples (NakedTensor) and is skilled at bridging theory and practice—for example developing the theory connecting proportional hazard losses to deep neural networks. Based in Dallas, he thrives on collaborative, cross-disciplinary work that moves scientific discoveries toward clinical and commercial impact. A fun, less obvious fact: his first job was planting grapes at a vineyard, where he left a bottle branded with his name.
code11 years of coding experience
job17 years of employment as a software developer
bookMcAuley Catholic High School
bookHigh School, Mathematics, High School, Mathematics at Missouri Southern State University
bookDoctor of Philosophy (Ph.D.), Computational Neuroscience, Doctor of Philosophy (Ph.D.), Computational Neuroscience at University of Chicago
bookBachelor’s Degree, Physics, Bachelor’s Degree, Physics at University of Arkansas
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Github Skills (7)

machine-learning10
tensorflow10
python10
linear-regression10
numpy9
matplotlib9
classification7

Programming languages (7)

JuliaC++JavaScriptGoHTMLJupyter NotebookPython

Github contributions (5)

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Neural network trained as a generative model on the MNIST dataset using Persistent Contrastive Divergence.
Contributions:136 commits, 2 PRs, 140 pushes in 4 years 7 months
generative-modelmnist-datasetneural-network
jostmey/rwa

Mar 2017 - Feb 2020

Machine Learning on Sequential Data Using a Recurrent Weighted Average
Contributions:108 commits, 4 PRs, 108 pushes in 3 years
machine-learningrecurrent-neural-networkssequential-datatime-seriesrwa-model
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