We are a high-growth U.S. startup on a mission to make options trading simple and accessible using AI, ML, and gamified learning. Join us to work on real-world trading models, uncovering patterns from data sources like stock price movements, volume, technical indicators, options flows, implied volatility, Greeks, macro trends, and sentiment.

What You’ll Do:

• Build predictive models using real financial data
• Analyze large and small datasets (structured & unstructured)
• Work on features that power our smart recommendation engine
• Analyze multi-modal datasets to find high-probability market signals
• Learn how AI drives product in a live startup environment

You’re a Fit If:

• You want to learn how a real startup operates, and how data drives product decisions
• You want to work at the edge of finance, AI, and behavioral psychology
• You enjoy solving hard, meaningful problems and are curious, driven, and want to learn fast
• You know Python (Pandas, Scikit-learn, etc.)
• You care about building impactful tech and are interested in finance or trading.

 What You Get

• Real-world ML experience and startup exposure
• A chance to build something that could be used by thousands (soon millions!)
• Mentorship from seasoned engineers and product leaders from top U.S. tech firms

In this project, students would work closely with our founding team to:

• Build and test predictive models that detect high-probability trading patterns
• Help improve our recommendation engine for retail traders, backed by explainable ML
• Identification of ways in which AI / ML can be applied to our company

Bonus steps would include:

• Providing multiple versions of potential models

Final deliverables should include

• A final report on the dataset, the problem solved, methodologies and approaches, outcomes and results, and recommended next steps.
• Source materials such as code and workbooks.