Nils Braun - Software Engineer at Apple

Nils Braun

Software Engineer at Apple

Heidelberg, Baden-Württemberg, Germany

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Summary

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Rockstar

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Top School

Nils Braun is a software engineer with 12 years of experience bridging data engineering, scientific research, and backend systems, currently building software at Apple from Heidelberg. He holds a PhD in Physics and has applied that analytical rigor to practical engineering problems—contributing to projects like tsfresh for time-series feature extraction and enhancing fsspec’s Databricks filesystem integration. Nils has strengthened SQL tooling and Dask-SQL support in Hue and improved compiler capabilities via Pythran support in Cython, showing a rare mix of data-science instincts and low-level systems work. He repeatedly focuses on robust implementations, test coverage, and maintainable refactors, and brings an eye for API correctness and performance that stems from both academic and industry roles.

12 years of coding experience

4 years of employment as a software developer

Doktor (Ph.D.), Physik, Doktor (Ph.D.), Physik at Karlsruher Institut für Technologie (KIT)

German, English, Swedish

Github Skills (31)

editors10

filesystem10

type-inference10

parser10

python10

feature-extraction10

data-science10

pandas10

databases10

time-series10

feature-engineering10

editor10

numpy10

fsspec10

parsing10

Programming languages (9)

TypeScriptC++ShellRustJavaScriptHTMLJupyter NotebookCython

Github contributions (5)

blue-yonder/tsfresh

Nov 2016 - Aug 2021

Automatic extraction of relevant features from time series:

Role in this project:

Data Scientist

Contributions:16 releases, 80 reviews, 184 commits in 4 years 9 months

Contributions summary:Nils contributed significantly to feature engineering within the time series analysis project. Their work focused on the implementation of new feature calculators for time series data, including features related to doubled values and statistical analysis. These additions involved modifying the core feature extraction code, adding testing functions, and improving documentation, enhancing the library's capabilities for feature extraction and analysis within the domain of time series data.

time-seriesdata-sciencefeature-extraction

fsspec/filesystem_spec

Dec 2020 - Jan 2021

A specification that python filesystems should adhere to.

Role in this project:

Back-end Developer

Contributions:7 commits, 1 PR, 11 comments in 14 days

Contributions summary:Nils primarily focused on implementing and refining the Databricks filesystem (DBFS) integration within the fsspec library. Their contributions included initial implementation, error handling, and implementing review comments, demonstrating a solid understanding of the DBFS API. They also addressed bug fixes related to the DBFS implementation and added unit tests to ensure functionality. Further work included refactoring the DBFS implementation for enhanced functionality and maintainability.

filesystempython

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