Chandini Jain
Founder CEO at Auquan
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolChandini Jain is the founder and CEO of Auquan, building AI agents for finance with eight years of experience at the intersection of trading, data science, and product. She began her career as a derivatives trader at Optiver and moved into quantitative research and fintech entrepreneurship, applying machine learning to real-world trading problems. Her GitHub work includes practical IPython tutorials demonstrating feature engineering, model selection, and PnL-focused evaluation for financial signals, reflecting a hands-on approach to ML in production. As a mentor in USC’s incubator she guided fintech and data science startups, blending academic rigor from an MS in Computational Science with street‑level trading intuition. Unusually, her background spans molecular-scale computational research to high-frequency trading, giving her a rare cross-domain perspective on modeling complex systems.
8 years of coding experience
2 years of employment as a software developer
Indian Institute of Technology KanpurMS Mechanical Engineering/Computational Science, MS Mechanical Engineering/Computational Science at University of Illinois Urbana-Champaign
Hindi, English
Github Skills (12)
linear-regression10
pandas10
machine-learning10
jupyter-notebook10
feature-engineering10
python10
data-analysis10
modeling9
statistical-models9
finance9
scikit8
scikit-learn8
Programming languages (2)
Github contributions (5)
Auquan/Tutorials
Sep 2017 - Jun 2018
Ipython notebooks for math and finance tutorials
Role in this project:
Data Scientist
Data ScientistContributions:16 commits, 1 PR, 15 pushes in 8 months
Contributions summary:Chandini's primary contribution involves applying machine learning techniques to financial trading strategies, as evidenced by the creation of an IPython notebook. The user details steps for creating a trading signal using historical data. They discuss data preparation, feature engineering, model selection and evaluation (RMSE, Total PnL), and provide insights on the pitfalls of applying ML to trading problems, along with code for linear regression model. They demonstrate the application of machine learning concepts to predict future basis values in a financial context.
mathpythonipython-notebooksquantitative-tradingnotebooks
Auquan/quant-quest-2
Sep 2017 - Mar 2018
Quant Quest 2
Contributions:21 commits, 20 pushes, 2 comments in 6 months
pythonquantquest
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