Stefan Jansen
Founder & Principal AI Consultant at Applied AI
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolStefan Jansen is a founder and Principal AI consultant with over a decade delivering production AI systems since 2015, moving organizations from strategy to revenue-generating deployments. He combines hands-on engineering—building, deploying, and operating ML pipelines and live trading infrastructure—with C-level advisory work across asset management, healthcare, and insurance. His projects include a patented-grade document extraction engine that cut contract analysis from an hour to seconds, customer-targeting systems that have served 30M+ decisions, and NLP signals used in investment workflows. Author of the widely used Machine Learning for Trading (companion repo >16k GitHub stars), he’s known for staying in the weeds rather than handing off work, yielding long-term client relationships that compound value. Based in New York, he blends a rare mix of domain finance experience, product instincts, and production-grade ML engineering.
10 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Master of Science - MS at Georgia Institute of TechnologyMaster of Public Administration / International Development, Master of Public Administration / International Development at Harvard UniversityMaster Business Economics, Master Business Economics at Freie Universität Berlin
German, English, Spanish, Portuguese, Indonesian, French
Github Skills (8)
financial-analysis10
machine-learning10
python10
data-science10
data-analysis10
backtest9
backtesting9
time-series9
Programming languages (9)
Github contributions (5)
stefan-jansen/machine-learning-for-trading
Feb 2019 - Jan 2023
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 4 reviews, 327 commits in 3 years 11 months
Contributions summary:Stefan contributed to bug fixes in the build_itch_order_book, environment configuration, and chapter 2 end-to-end test cases, demonstrating their involvement in improving the project's functionality and overall stability. They also made changes to chapter 4's test and alphalens environment configurations, which indicate their work on factor research and performance evaluation, including adjustments to the feature engineering.
algorithmic-tradingtrading-agentdata-sciencedeep-learninginvestment-strategies
stefan-jansen/zipline-reloaded
Feb 2021 - Sep 2024
Zipline, a Pythonic Algorithmic Trading Library
Contributions:9 releases, 3 reviews, 109 PRs in 3 years 7 months
algorithmic-trading-librarypythonalgorithmic-tradingtrading-botbacktesting-trading-strategies
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