How are candidate match qualities ranked?

ScaleUp automatically scores/groups each candidate into either a:

• High priority candidate, or "Great Match"
• Priority candidate, or "Good Match"
• Requirements met, or "OK Match"
• Criteria not met, or"Poor/Not a Match"

The relevant ranking of each candidate search result page is based on relevance to your requirements like Job title, Location, Companies, etc., as well as criteria such as:

• Ensuring all Must Have skills are included in the candidate's profile
• How many Nice-to-Have skills the candidate fulfills

You can filter match status to show a preferred list of candidates based on match status. Click the filter icon and sort by your preference.

Note:

In terms of how candidates are listed/displayed by default (without filtering), the most qualified will appear first. Candidates fall into "buckets" of qualifications. If one or more candidates have exactly (or near exactly) the same qualifications, they're grouped together as a "bucket" in the list, but there is no logic that determines one candidate appear before the other.

To locate the candidates, ScaleUp uses a number of data sources. The top 5 categories of data collection sources are:

1. Profile data set: A consolidation of fragmented people data into a massive data lake. This could include public resumes (LinkedIn profiles), public code profiles (GitHub/Stack Overflow), patents, publications etc.
2. Company product categories (time indexed): Indices of companies by product and product categories
3. Company financial data set (time indexed): Public filings, public financials, funding rounds including investor profiles for private companies
4. Skills data set: Consolidation of all publicly indexed and derived skills from multiple data sets
5. Other contributions and social profiles

Attributes are derived from the comprehensively indexed people data and attributes are used for indexing people as well as capturing hiring intent. Ultimately, attributes are the avenues to capture the overall results (think about attributes as a complex database query spanning across multiple dataset. This allows one to express pretty much anything that one can say in plain English).