Composite scoring - TypeSafe AI

Oftentimes we want to rank a set of items based on several criteria at once. Composite scoring is an easy way to think about this: break the judgment into independent dimensions, score each one separately, and combine them with weights you control in code.

Example: resume screening

Let’s imagine you are processing resumes for engineering roles. You want to rank the candidates based on several criteria, and ultimately select the top X candidates for further review.

Step 1: score each dimension independently

Step 2: combine with weights

Each dimension is normalized to 0–1 and weighted. The weights give you an easy way to adjust the relative importance of each dimension, without losing any of the nuance of the individual scores.

scoring.py

py      = response.answers["python_depth"].score / 4
lead    = response.answers["team_leadership"].score / 4
arch    = response.answers["system_design"].score / 4
general = response.answers["generalist"].score / 4

# Senior IC
ic_score = (0.40 * py) + (0.10 * lead) + (0.40 * arch) + (0.10 * general)

# Engineering Manager
em_score = (0.15 * py) + (0.40 * lead) + (0.20 * arch) + (0.25 * general)

This gives you the ability to rank the candidates based on the composite score. But more importantly, it gives you visibility into how exactly the final score is being calculated. If the highest ranking candidates are not matching your expectations, you can adjust the weights to find the right balance.