Criteria Definition and Weight Calibration Best Practices

A- Criteria definition

General Concepts

Defining resume screening criteria in Brainner is a crucial part of the process, as our AI will follow your instructions precisely to evaluate candidates’ resumes based on the specified requirements.

Our AI can perform semantic searches, meaning it understands context, synonyms, and jargon. Unlike typical keyword matching, you don’t need to be overly specific with every word, but you do need to be accurate in explaining, detailing, and quantifying what you truly need from candidates.

Best Practices

1- The more specific the criteria, the more accurate the results.

Example 1

“Experience working at Google” – The criteria will be met if the candidate has worked at Google.

“Experience working at companies like Google” – The criteria will be met if the candidate has worked at companies similar to Google, but this is still somewhat vague, and the analysis may be subjective.

“Experience working at global tech companies like Google with operations in the USA” – This is a more specific criterion that allows our AI model to conduct an objective analysis.

Example 2

“5+ years of experience as a Purchasing Manager in the retail industry” – This is a clear and specific criterion. Our AI model will award the full score only if the candidate meets all aspects of the criterion. If only some are met, the score will be reduced to half.

You can also choose to break this down into two separate criteria with different weights:

• “5+ years of experience as a Purchasing Manager” – Full score if the candidate meets all conditions; a partial score if, for example, they have only 2 years of experience.

• “Experience in the retail industry” – Full score if the candidate has worked in the retail industry, even if not as a Purchasing Manager.

2- Write criteria that answer the question: “What requirement should the candidate meet?”

Example 3

“Must be based in the USA” (Correct)

“USA” (Incorrect)

Example 4

“5+ years of experience with TypeScript” (Correct)

“TypeScript” (Incorrect)

3- Write clear criteria, avoiding abbreviations.

Example 5

“Holds a degree in Business Computer Science” (Correct)

“Holds a degree in BCS” (Incorrect)

4- Quantify work experience and skills to ensure accurate seniority and relevant experience.

Example 6

“Experience as a Back-End Developer” – This could result in a full score for just 1 year of experience (Not specific).

“5+ years of experience as a Back-End Developer” – Full score only if the candidate meets the 5-year requirement. Partial score if they meet the experience but not the years (Correct).

Example 7

“Experience with Python” – Full score could be awarded even if the experience was 10 years ago (Not specific).

“Experience with Python in the last 2 jobs” (Correct)

Example 8

“2 years of experience with SAP” (Incorrect. It might imply that the candidate should have only 2 years of experience)

“2 or more years of experience with SAP” (Correct)

5- Use Boolean strings (AND, OR, NOT) for skills or work experience criteria.

Example 9

When the candidate must meet all skills requirements: “5+ years of experience with Adobe Premiere AND Adobe Illustrator AND Adobe Photoshop.”

Example 10

When the candidate must meet at least one of the skills requirements: “Experience with Adobe Premiere OR Final Cut Pro in the last two positions.”

Example 11

When you want to exclude specific experiences:

“Experience in sales BUT NOT as a Customer Success Representative.”

6- Decide whether to use location as a criterion.

Option A: Include location in the candidate profile information to filter candidates based on location. In this case, location won’t affect the scoring.

Option B: Include location as a criterion. In this case, location will impact the final score.

Example 12

“Must be based in the USA.”

Example 13

If you’re looking for candidates from different locations or regions, use OR logic:

• “Must be based in California OR New York OR Miami.”

• “Must be based in South America OR Central America.”

7- Include criteria not listed in the job description but essential for resume screening.

Example 14

“2+ years of experience at McDonald’s, Burger King, or KFC.”

8- Choose whether to consider the frequency of job changes as a criterion.

Some companies evaluate how frequently candidates change jobs. This can be controversial, as frequent changes may be due to circumstances beyond the candidate’s control, such as layoffs.

Example 15

“Has spent at least 2 years in each of the last 3 jobs.”

9- Choose wheter to consider soft skills as screening criteria.

Evaluating soft skills such as communication skills or leadership abilities from a resume can be subjective. However, our AI can provide an assessment of some soft skills based on resume content. Some users choose to include this information to complement the decision-making process

B- Criteria weight definition

General concepts

Mandatory criteria represent 80% of the total weight, while preferred criteria account for 20%. It’s important to understand how much each criterion will weigh before starting the analysis.

Example 16

If you have 5 mandatory criteria and 5 preferred criteria:

• Each mandatory criterion will weigh 16%

• Each preferred criterion will weigh 4%

Example 17

If you have 5 mandatory criteria and 1 preferred criteria:

• Each mandatory criterion will weigh 16%

• Each preferred criterion will weigh 20% (more than the mandatory ones!). I In this case, you may choose to either delete the preferred criterion or add new preferred criteria)

Example 18

If you have 5 mandatory criteria and 0 preferred criteria:

• Each mandatory criterion will weigh 16%, and the maximum possible score will be 80%.

C- Scoring calculation

General concepts

• Met criteria: 100% of the score

• Partial criteria: 50% of the score

• Unmet criteria: 0% of the score

Example 19

• Each mandatory criterion weighs 26,7%.
• Met criterion contributes with 26,7%
• Partial criterion contributes with: 13,4%
• Unmet criterion contributes with: 0%

• Each preferred weigh 6,7%
• Met criteria contribute with 20% (6.7% each)

Total score: 26,7% + 13,4% + 20% = 60%

D- Criteria definition and weight calibration

We recommend initially calibrating the criteria definition and weights with a small sample to ensure that:

• The criteria are well-defined, and the output aligns with what you are looking for in that specific requirement.

• The weight assigned to each criterion accurately reflects your hiring goals.

Once the criteria are calibrated, you can proceed with re-evaluating those candidates and adding the complete batch.