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ML Jobs Newsletter - Issue #20

ML Jobs Newsletter - Issue #20
By • Issue #20 • View online
We’re back from a whirlwind tour of ODSC East, PyCon, and ICLR, which means our regularly scheduled programming below:
Joel Grus of “I Don’t Like Notebooks” fame also presented “Reproducibility as a vehicle for engineering best practices” at ICLR this past week. He shared why engineering best practices around version control, unit testing, reproducibility, etc.. should still apply to data science. To that end, Joel shared a cool tool called Beaker, but unfortunately it is internal to the Allen Institute’s AI2 organization. If you are interested in automatically tracking and organizing your ML models to be reproducible, try! ⭐️
On the career side:

Curated with love by
Visualize your model's performance automatically and retrieve untracked changes even when you haven't committed them yet!
Visualize your model's performance automatically and retrieve untracked changes even when you haven't committed them yet!
Comet is doing for ML what GitHub did for code. We allow data science teams to automatically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.
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This bimonthly newsletter gives you access to the latest machine learning roles across the world.
If your company is hiring for awesome data scientists, machine learning engineers, or research scientists and would like to be included here, send an email to me at
Featured ML Job:
[Basking Ridge, NJ]
As the Principal Data Scientist at Verizon, you will develop quantitative and computational solutions using big data, statistical analyses, machine learning, optimization and related analytical techniques to solve complex supply chain challenges. In addition, you will enable the architecture required to advance analytical solutions in our supply chain ecosystem.
This position requires excellent communication and leadership skills to lead conceptualization, prototyping, testing, and execution of advanced statistical models and machine learning algorithms, and drive financial results and stakeholder engagement.
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