Template
Screening scorecard for AI-first hiring.
Use this structure to connect Merqent scores to pre-agreed criteria and human judgment.
1. Role criteria upfront
Before candidates arrive, define which signals are actually needed for success in the role. Split hard requirements, learnable skills and preferences.
- Must-have skills
- Experience level
- Context someone needs to perform in
- Knock-outs that can be explained upfront
2. Evidence per criterion
A score is only useful when it is clear which answer, example or work sample supports it.
- Concrete interview situation
- Outcome or impact
- Tradeoff or decision
- Work sample or case output
3. Recruiter review
Use the AI score as an input, not the final decision. The reviewer should be able to flag deviations and make risks explicit.
- Why continue
- Why reject
- What to probe live
- Which doubt is still open
Rubric you can reuse
5
Strong evidence
Concrete example, relevant to the role, with clear impact and reflection.
4
Good evidence
Relevant example with enough depth, but still one follow-up question open.
3
Mixed evidence
General answer or only partly relevant evidence.
2
Weak evidence
Not very concrete, role relevance unclear or too many assumptions required.
1
No evidence
No usable answer or evidence that goes against the role requirement.
Copy block for your intake
For this role we assess candidates on four criteria: role-relevant experience, problem solving, communication and evidence from work. A candidate only moves forward if there is concrete proof for the must-haves and a recruiter has reviewed the AI summary.