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Kalshi

Archived

Forecasting weekly TSA checkpoint volume from federal data to trade the Kalshi prediction market profitably.

Project Manager
Eubene In

Finance Time Series Modelling
Ran
Fall 2025 – Spring 2026
Commitment
4-5 hrs/week
Openings
2-3

What you need coming in

Comfortable building — or learning to build — ML models from scratch. Intermediate Python with Pandas, NumPy, and Matplotlib for data manipulation and trend visualization. Creative feature engineering and data augmentation are valued. Basic ML concepts help: diagnosing failure points, identifying overfitting, and proactively seeking improvements.

Description

Kalshi is a prediction market where anybody can “bet on anything.” It carries a lot of niche markets — betting on the highest temperature in major cities, for instance. Another is betting on the average daily TSA check-ins during a given week. Every week when this market opens, a plethora of vouchers open that give a buyer the option to bet on whether average check-ins land over or under a given amount.

The U.S. Government posts daily-level data going back to 2019 on how many people checked in through TSA checkpoints. That opens the opportunity to develop a model (or models) to predict weekly average check-ins at a given point in time. With a strong enough model, we can make confident predictions, bet accordingly, and take a weekly payout.

Other projects build working computer vision implementations or win competitions worth putting on your portfolio. This one offers both model-building experience and a profit motive. With a dedicated team, it has the potential to produce quantifiable results in the form of real returns.

Note: We are approaching the paper-trading phase. If results are consistently profitable, the PM will fund live trades personally and share profits with the team.

Each semester we look to recruit one beginner and one intermediate (or above) member — so don’t be afraid to apply regardless of your current skill level.