# When Career Data Runs Out: Structured Feature Engineering and Signal Limits for Founder Success Prediction

arXiv: https://arxiv.org/abs/2604.00339 · HTML: https://mlsanity.com/paper/2604.00339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning](https://mlsanity.com/paper/2505.21427/) (2025)
- [VCBench: Benchmarking LLMs in Venture Capital](https://mlsanity.com/paper/2509.14448/) (2025)

## Abstract (arXiv)

Predicting startup success from founder career data is hard. The signal is weak, the labels are rare (9%), and most founders who succeed look almost identical to those who fail. We engineer 28 structured features directly from raw JSON fields -- jobs, education, exits -- and combine them with a deterministic rule layer and XGBoost boosted stumps. Our model achieves Val F0.5 = 0.3030, Precision = 0.3333, Recall = 0.2222 -- a +17.7pp improvement over the zero-shot LLM baseline. We then run a controlled experiment: extract 9 features from the prose field using Claude Haiku, at 67% and 100% dataset coverage. LLM features capture 26.4% of model importance but add zero CV signal (delta = -0.05pp). The reason is structural: anonymised_prose is generated from the same JSON fields we parse directly -- it is a lossy re-encoding, not a richer source. The ceiling (CV ~= 0.25, Val ~= 0.30) reflects the information content of this dataset, not a modeling limitation. In characterizing where the signal runs out and why, this work functions as a benchmark diagnostic -- one that points directly to what a richer dataset would need to include.