Aly Sivji is a product-focused software engineer with a decade of experience building data-driven HealthIT solutions and leading platform teams across startups and enterprise settings. Comfortable shipping end-to-end systems, Aly has driven integrations for RPM, FHIR, and HL7v3, migrated architectures to microservices and Kubernetes, and led data pipeline and DevOps efforts that bridge clinical and engineering needs. An active open-source contributor, they’ve improved pandas, pytest, and visualization tooling—adding practical features like read_excel nrows and robust legend handling—demonstrating attention to usability and testing. A clear communicator and community organizer who speaks at conferences and blogs about craft, Aly also studies improv at The Second City, bringing empathy and improvisational problem-solving to product and team leadership. Currently based in Chicago and open to new opportunities in HealthIT, they blend technical depth in Python and data with a passion for evidence-based, personalized medicine.
10 years of coding experience
6 years of employment as a software developer
Master of Science (MS) Cybersecurity, Master of Science (MS) Cybersecurity at Georgia Institute of Technology
Master of Science (MS) Medical Informatics, Master of Science (MS) Medical Informatics at Northwestern University
Bachelor of Mathematics (BMath) Computational Mathematics, Bachelor of Mathematics (BMath) Computational Mathematics at University of Waterloo
The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:1 PR, 10 comments in 2 years
Contributions summary:Aly focused on enhancing the testing framework of the pytest repository. Their contributions involved adding and updating tests, specifically for dataclasses and attrs classes. The user refactored and improved existing test cases, while also improving the output of the test failures. The work included moving and cleaning up test files, making the codebase more maintainable.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
Data Scientist
Contributions:9 commits, 10 PRs, 36 comments in 5 months
Contributions summary:Aly contributed to the pandas library by adding a `nrows` parameter to the `read_excel()` function, allowing users to specify the number of rows to parse. They also addressed a bug related to passing keyword arguments from `Index.to_series` to `pd.Series`, ensuring correct behavior in various scenarios. Further contributions included organizing and cleaning up test files related to the groupby aggregate functionality, and improving documentation for the `pandas.Index.repeat` method.
pythondatalabeled-datamanipulationdataframes
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