Joseph Berry
Senior Machine Learning Engineer at Red Hat
Israel
Summary
👤
Senior🎓
Top SchoolJoseph Berry is a Senior Machine Learning Engineer with 10 years of experience building scalable ML infrastructure and data pipelines across GCP and AWS. He combines an MLOps-first mindset with hands-on expertise in distributed data frameworks (Spark, Dask), pipeline tooling (Dagster, dbt), and reproducible research workflows to move models from prototype to production. At companies from government to startups and now Red Hat, he has led cross-functional teams, applied PMP-backed project management, and created internal Python libraries that standardize ML delivery. His open-source contributions include practical Dask examples that clarify Pandas vs. Dask behaviors—evidence of his focus on developer ergonomics and performance. Skilled with diverse databases (relational, spatial, graph, columnar) and fluent in Python, he uniquely blends GIS-rooted analytical thinking with cloud-native MLOps engineering. Based in Israel, he is adept at translating messy, large-scale data into reliable, operational AI services.
10 years of coding experience
16 years of employment as a software developer
B.Sc. Environmental Science & Geography, B.Sc. Environmental Science & Geography at The Hebrew University of JerusalemUMEHartman High SchoolUniversity College London
English, Hebrew
Github Skills (4)
dask10
pandas10
python10
data-analysis10
Programming languages (10)
Github contributions (5)
dask/dask-examples
Apr 2019 - Jun 2019
Easy-to-run example notebooks for Dask
Role in this project:
Data Scientist
Data ScientistContributions:15 commits, 1 PR, 16 comments in 2 months
Contributions summary:Joseph contributed to the development of examples for Pandas vs. Dask dataframes, focusing on common operations like renaming, resetting the index, date conversions, and handling missing values. Their work involved direct manipulation of Dask and Pandas DataFrames to showcase the differences in these operations. The user also showed the use of meta and how this influences the use of apply in dask.
dask
sephib/dask_pyconil2019
May 2019 - May 2021
presentation on dask framework - in context of ETL when coming from Pandas
Contributions:24 commits, 1 PR, 19 pushes in 1 year 11 months
dasketlpandas
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