Carlos Paradis is a data scientist with 14 years of multidisciplinary experience, currently contracting with KBR to support NASA on safety and threat detection. He holds advanced degrees including a Ph.D. whose dissertation produced an automated model to surface emerging safety risks from NASA’s ASRS, and his research spans cybersecurity, social-content analysis, and econometric studies of software engineering. Carlos has applied his skills across healthcare analytics at Kaiser Permanente, building production-ready predictive systems, and in university research where he led student teams to deliver reusable data products and tooling (e.g., the Lonoa sensor-monitoring tool). Based in Sunnyvale, he blends rigorous academic training with hands-on engineering—managing data pipelines, sensor networks, and reproducible workflows—while contributing to open-source projects on GitHub. He is also a Science Without Borders alumnus and a member of IEEE and ACM, reflecting a sustained commitment to scholarly and professional communities.
14 years of coding experience
11 years of employment as a software developer
Bachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Universidade Federal da Bahia
Master's degree Computer Software Engineering, Master's degree Computer Software Engineering at Stevens Institute of Technology
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at University of Hawaii at Manoa
Contributions:2 PRs, 11 pushes, 1 comment in 2 years 5 months
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