Peter Hansen
Senior Data Scientist
Minneapolis, Minnesota, United States
Summary
👤
Senior🎓
Top SchoolPeter Hansen is a Senior Data Scientist and physicist with a PhD from the University of Minnesota and a decade of experience turning complex data into actionable insight. He brings rigorous experimental and simulation expertise from high-energy physics at CERN to industry problems, shipping efficient algorithms, robust data validation, and clear visualizations that surface uncertainty rather than hide it. At phData and prior roles he’s built production-ready analytics and contributed to notable open-source projects like Great Expectations and the matrixprofile-ts library, improving data profiling and motif-detection algorithms. Comfortable across statistics, simulation, and software engineering, he excels at translating domain complexity into reproducible pipelines and tests. Colleagues rely on him for precise measurement thinking and for balancing mathematical rigor with pragmatic product delivery.
10 years of coding experience
14 years of employment as a software developer
Degree: Bachelor of Science, Physics; Mathematics, Degree: Bachelor of Science, Physics; Mathematics at University of Nebraska-LincolnUniversity of Minnesota Twin Cities
Github Skills (16)
algorithm10
data-structures10
algorithms10
time-series10
data-quality10
tqdm10
python10
data-science10
data-structure10
data-profiling10
numpy10
data-engineering10
pytest9
pandas9
sqlalchemy8
Programming languages (9)
Github contributions (5)
target/matrixprofile-ts
Aug 2019 - Aug 2019
A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile
Role in this project:
Data Scientist
Data ScientistContributions:16 commits, 4 PRs, 5 comments in 7 days
Contributions summary:Peter primarily contributed to the development of motif discovery algorithms within the matrixprofile-ts library. Their work includes implementing and testing functions related to motif identification, exclusion zones, and nearest neighbor searches. They also added unit tests to verify the correctness of motif detection, demonstrating a focus on algorithm accuracy and functionality. Furthermore, the user included documentation through example notebooks.
pythonpypipipmotifpypi-packages
great-expectations/great_expectations
Feb 2021 - Jul 2021
Always know what to expect from your data.
Role in this project:Data Engineer
Data EngineerContributions:6 commits, 6 PRs, 10 comments in 4 months
Contributions summary:Peter contributed to the Great Expectations project by addressing bugs, adding enhancements, and making code improvements. Their work included fixing date parsing and overflow exceptions in the basic profiler, ensuring column comparisons were always on the left-hand side, and adding a progress bar using `tqdm` to several profiling and validation processes. They also corrected an issue related to expectation calculation in the `expect_column_kl_divergence_to_be_less_than` function.
pythondatadata-integritydatacleanerpipeline-testing
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.