Context

Insight Global uses a binary classification model in its recruiter workflow to predict whether a candidate lead in the Insight Global talent platform will respond positively to outreach within 7 days. Recruiters can only prioritize about 1,200 leads per day, so the operating threshold directly determines both recruiter workload and missed opportunities.

The current model was trained on 1.8M historical leads and outputs a probability score. The team is debating whether to keep the current threshold of 0.50 or move to a lower threshold to capture more likely responders.

Current Performance

The Problem

Recruiting leadership wants more candidate responses, but recruiter operations cannot exceed daily review capacity for long. You need to recommend an operating threshold and explain the tradeoff clearly.

Requirements

1. Identify which threshold you would recommend and why.
2. Quantify the precision-recall tradeoff using the table.
3. Explain how business costs of false positives vs. false negatives should influence threshold choice.
4. Discuss whether calibration matters before using the score operationally.
5. Propose how you would validate the threshold after launch.

Constraints

• Recruiter capacity is ~1,200 prioritized leads/day.
• A false positive wastes recruiter time and delays other outreach.
• A false negative means a potentially responsive candidate is not prioritized.