Tom Drabas
Senior Developer Relationship Manager
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolTom Drabas is a Senior Developer Relationship Manager and principal data scientist with 11+ years of experience building GPU-accelerated ML and big-data solutions, currently working at NVIDIA in Seattle. He combines deep academic credentials—a PhD in Operations Research focused on airline revenue management—with hands-on engineering across Spark/PySpark, RAPIDS/cuDF, TensorFlow/PyTorch and SQL-based analytics. Tom is an author of Learning PySpark and Practical Data Analysis Cookbook and has contributed code and documentation to prominent open-source projects like BlazingSQL and RAPIDS community notebooks, bridging developer advocacy and production ML. He has a track record of turning research-grade choice and clustering models into deployable pipelines and has driven anomaly-detection and market-penetration methodologies at Microsoft. Known for pragmatic evangelism, he often pairs performance-tuned GPU workflows with clear developer docs and reproducible notebooks—a less visible but recurring strength across his roles.
10 years of coding experience
13 years of employment as a software developer
MA Strategy Management, MA Strategy Management at Wyższa Szkoła Biznesu - National Louis University w Nowym SączuUNSW Sydney
English, Polish, French
Stackoverflow
858reputation
139kreached
16answers
2questions
Github Skills (25)
restructuredtext10
python10
sphinx10
machine-learning10
cudf10
pyspark10
rs10
ml10
data-transformation10
rapids10
jupyter-notebook10
documentation10
data-analysis10
data-visualizations9
data-visualisation9
Programming languages (9)
Github contributions (5)
drabastomek/learningPySpark
Jul 2016 - Apr 2019
Code base for the Learning PySpark book (in preparation)
Role in this project:
Data Scientist
Data ScientistContributions:33 commits, 30 PRs, 29 pushes in 2 years 9 months
Contributions summary:Tom contributed code examples and content related to learning PySpark for data processing and analysis. The commits demonstrate the use of PySpark for creating RDDs, data transformation, and working with different data types. Key areas of focus include data preparation techniques such as removing duplicates and handling missing values, as well as the application of machine learning models from MLlib, including logistic regression and decision trees.
preparationpythonsparkpysparkbase-code
BlazingDB/blazingsql
Mar 2021 - Apr 2021
BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.
Role in this project:Technical Writer
Technical WriterContributions:82 commits, 4 PRs, 1 push in 1 month
Contributions summary:Tom primarily contributed to the project by restructuring the documentation folders and adding new documentation for use cases. They integrated external data sources into the documentation by including CSV data, and focused on defining the structure for the SQL API reference. The commits demonstrate an effort to improve the organization and accessibility of the project's documentation.
cudfgpu-accelerationpythondata-sciencegpu
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