Screening Criteria define how Mokka evaluates candidates. They combine hard requirements, context signals, quality checks, and candidate location data to shape scoring and recruiter visibility. The 'Excellence' and 'Accomplishments' interview segments are disabled by default for new roles.
1. Responsibilities
Why it matters:
AI matches candidate CV responsibilities against the job description and feeds this signal into the overall score.
Best practice:
Keep them specific (avoid "general responsibilities").
Make sure they reflect day-to-day role expectations, not just seniority.
2. Requirements (Minimum, Must-have, Nice-to-have)
Why it matters:
The most direct pass/fail criteria in Mokka.
Minimum → deal-breakers. Missing a minimum significantly lowers the candidate's ranking and pushes them into the Low fit tab. Use minimums to filter out irrelevant applications quickly. They work in parallel with application questions, since applicants may not always answer application questions truthfully or correctly. Using minimum requirements to filter on, for example, years of experience within a certain domain can help surface top applicants faster.
Must-have → strong influence on ranking. Missing a must-have does not disqualify the way a minimum does, but candidates without them rank lower. If there are no minimum requirements, must-haves carry most of the weight.
Nice-to-have → softer signals that help tie-break between otherwise strong candidates.
Best practice:
Limit Minimum to true deal-breakers (2–3 max).
Keep Must-haves focused on essentials.
Use Nice-to-haves sparingly to avoid noise.
3. Goals
Why it matters:
AI checks whether candidate achievements align with the role's success criteria.
Candidates see this question and share their most relevant experience.
Best practice:
Define measurable goals (e.g. "Increase revenue by 20%") instead of vague ambitions.
Use them to highlight the success factors the hiring manager cares about.
4. Years of Experience
Why it matters:
Candidates are scored relative to where they sit between the minimum and ideal values you set. Candidates below the minimum rank lower.
Best practice:
Always set both minimum and ideal.
Use realistic ranges (avoid "10+ years" unless truly needed).
5. Frequent Job Changes Filter
Why it matters:
Mokka flags candidates with frequent short tenures as a stability risk.
You choose how strongly to weight this signal — or turn it off entirely for roles where it isn't relevant (early-career, contract, freelancing).
Best practice:
Enable only for roles where tenure is material.
Even with the flag, review context — freelancing or industry norms may explain movement.
6. Profile Integrity Check
Why it matters:
Detects inconsistencies across CV, LinkedIn, answers, and other signals.
Risk is surfaced as:
• Green (no concerns)
• Amber (minor inconsistencies worth reviewing)
• Red (serious discrepancies — surfaced with reasoning)
Does not affect scores directly.
Best practice:
Treat as a warning, not a rejection.
Review high-risk candidates manually — data errors can occasionally trigger false positives.
✅ Tips for Recruiters
Think of Requirements as gates, while Responsibilities and Goals shape fit quality.
Use years of experience and job change filters carefully to avoid over-penalizing good candidates.
Integrity checks are there to help you investigate, not eliminate.
Keep criteria balanced — too many restrictions can shrink your talent pool unnecessarily.