Jacob Baumbach
Deep Learning Engineer at Susquehanna International Group, LLP (SIG)
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJacob Baumbach is a Deep Learning Engineer in San Francisco with a decade of experience building production-grade ML and data systems for finance and industry. He currently develops deep learning solutions at Susquehanna after earlier roles engineering high-throughput Scala pipelines, Spark jobs, Flink and Kafka-based validation systems at Capital One that processed 75M+ messages per day. His background blends mathematical economics and data science (University of the Pacific, LSE, Galvanize) with hands-on research into recommendation algorithms and large-scale model sweeps. Jacob pairs functional-programming discipline (Scala/Haskell) with practical ML engineering, and has a track record of teaching peers and operationalizing complex analytics into reliable, exactly-once distributed systems. Less obvious: he began his career as an economic analyst and research assistant, giving him a rare mix of econometric rigor and production ML experience.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science (BS), Mathematical Economics, 3.85, Bachelor of Science (BS), Mathematical Economics, 3.85 at University of the PacificMaster of Science (M.S.), Data Science, Master of Science (M.S.), Data Science at Galvanize - San Francisco, SoMaGeneral Course (Study Abroad, Statistics/ Economics/ Finance, 3.6, General Course (Study Abroad, Statistics/ Economics/ Finance, 3.6 at London School of Economics and Political Science
Github Skills (45)
toolbox10
in-memory10
forge10
java10
parquet10
apache9
conda9
apache-arrow9
neo4j-client8
interchange8
data-interchange8
conda-forge7
networkx7
memgraph6
neo4j6
Programming languages (4)
Github contributions (5)
jacobBaumbach/trendOff
Jul 2016 - Jul 2016
Contributions:3 commits, 2 pushes, 1 branch in 1 day
jacobBaumbach/StreamKDE
Dec 2016 - Dec 2016
Contributions:5 commits, 4 pushes, 1 branch in 1 day
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