Scott Daniel
Software Engineer at Allen Institute for Brain Science
Greater Seattle Area United States
Summary
👤
Senior🎓
Top SchoolScott Daniel is a software engineer with 13 years of experience specializing in transforming large scientific datasets into actionable insights using Python, SQL, and C++. Currently on the information architecture team at the Allen Institute, he builds workflows that fuse transcriptomics, electrophysiology, and morphology analyses for the global neuroscience community. His background in astrophysics and cosmology—spanning database management for 20 TB LSST simulations to Bayesian inference with Gaussian Processes—gives him deep expertise in scalable data pipelines and statistical modeling. An active contributor to the AllenSDK, he has improved time-series processing and quality-control for optical physiology datasets, ensuring accurate frame-to-timestamp alignment. Driven by curiosity, he combines rigorous academic training (PhD in Physics) with pragmatic engineering to solve messy, cross-disciplinary data problems.
13 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at Dartmouth CollegeBachelor of Arts - BA, Physics and Astronomy, summa cum laude, Bachelor of Arts - BA, Physics and Astronomy, summa cum laude at Whitman College
Github Skills (10)
pandas10
python10
data-processing10
numpy10
data-analysis10
scientific-computing9
data-validation9
bioinformatics8
version-control7
git7
Programming languages (13)
Github contributions (5)
AllenInstitute/AllenSDK
Jan 2021 - Sep 2022
code for reading and processing Allen Institute for Brain Science data
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:211 reviews, 678 commits, 113 PRs in 1 year 8 months
Contributions summary:Scott's contributions focused on improving the data processing pipelines within the AllenSDK repository, specifically related to analyzing visual behavior data, including processing trials and implementing quality control steps in dataframes for time-series data, likely for optical physiology experiments. The user worked on data transformations for behavior and ophys sessions, reordering and validating data for analysis, and incorporated quality metrics and exception handling, demonstrating a strong understanding of data processing techniques and data organization practices for scientific datasets. They also modified code that handles the mapping between raw frames and timestamps to align them correctly, contributing to the accuracy of the processed data.
brainneurosciencepythonscienceneuroimaging
lsst/sims_catUtils
Jan 2014 - Oct 2019
LSST Simulations package for catalog utilities
Contributions:2 releases, 3620 commits, 146 PRs in 5 years 10 months
lsstcatalogsimulations
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