Nathan Segerlind
Data Science Software Engineer
Beaverton, Oregon, United States
Summary
👤
Senior🎓
Top SchoolNathan Segerlind is a Data Science Software Engineer with a decade of experience applying rigorous mathematical methods to real-world problems, shipping production analytics and scalable big-data code at Intel. He blends deep theoretical training (PhD in Computer Science, extensive research and publications in algorithms) with hands-on engineering across Scala, Spark, Python and C/C++, and has led small technical teams to deliver features adopted into product. His work on network anomaly detection and contributions to the Apache Spot project show practical expertise in netflow/DNS analysis, probabilistic modeling and graph-based intrusion detection. He also built production-ready graph analysis and ETL pipelines (Hadoop, Pig, graph databases) and has a track record of translating customer needs into implemented prototypes and product features. Based in Beaverton, OR, he pairs academic rigor with pragmatic delivery and a penchant for turning abstract models into working systems.
10 years of coding experience
7 years of employment as a software developer
University of California San DiegoBachelor of Science (BS), Computer Science and Mathematics Double Major, 3.9, Bachelor of Science (BS), Computer Science and Mathematics Double Major, 3.9 at Carnegie Mellon University
Github Skills (10)
netflow10
spark10
dns10
python10
data-analysis9
machine-learning9
scala8
data-pipeline8
data-pipelines8
statistical-models8
Programming languages (1)
Github contributions (5)
apache/incubator-spot
Feb 2017 - Jun 2017
Mirror of Apache Spot
Role in this project:
Back-end Developer
Back-end DeveloperContributions:17 commits, 2 PRs, 68 comments in 4 months
Contributions summary:Nathan primarily contributed to the Apache Spot project, focusing on netflow and DNS analysis. Their commits demonstrate work on the suspicious connects analysis, including cleaning data, fitting probabilistic models, and identifying outliers. Furthermore, they are also involved in tuning the flow analysis, including the incorporation of protocol information, and binning time, byte count, and packet count to optimize the model.
apachebig-dataspotcybersecurityjava
Open-Network-Insight/open-network-insight
Oct 2016 - Jan 2017
Contributions:63 commits, 30 PRs, 15 pushes in 2 months
packetsthreatsdnswiresharkhadoop
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