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

• URL: http://arxiv.org/abs/2604.00339v1
• Date: Wed, 01 Apr 2026 00:28:53 GMT
• Title: When Career Data Runs Out: Structured Feature Engineering and Signal Limits for Founder Success Prediction
• Abstract summary: Predicting startup success from founder career data is hard.<n>We engineer 28 structured features directly from raw fields -- jobs, education, exits -- and combine them with a deterministic rule layer and XG boosted stumps.<n>Our model achieves Val F0.5 = 0.3030, Precision = 0.3333, Recall = 0.2222 -- +17.7pp improvement over the zero-shot LLM baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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