Make adjustments to your Criteria to hone in on your ideal candidates. To edit your criteria, follow the steps in this article.
Search Filters
š¤ Personas
ā What are personas?
ā What are personas?
Dover uses machine learning to define personas through analysis of current titles, previous titles, and keywords associated with the work under the selected persona.
Dover scans LinkedIn profiles to search for titles, in both the "Headline" section and the "Experience" section.
š¤ Dover's Personas
š¤ Dover's Personas
Use the section below to determine which title patterns youād like to target in your search.
Dover has a list of pre-defined personas that each have a list of titles patterns within its definition. Some personas also use machine learning to scan the candidateās full profile to determine if the candidate is a match. For example, Doverās āFrontend Engineerā persona has 20+ title patterns within its definition (e.g. āfrontend engineerā, āUI/UX developer,ā āsoftware engineer, frontendā) and also scans profiles with generic title patterns like āsoftware engineerā to see if candidates are focused on the frontend.
Dover personas also use ādealbreakerā logic to ensure accuracy. For example, this logic would help to block titles like āSoftware Engineer Sourcerā from an engineering search.
Titles must be an exact match, so if you input āsoftware engineer," candidate titles such as āsoftware engineeringā and āsoftware development engineerā will not match.
š Create a new Custom Persona
š Create a new Custom Persona
If you don't want to use Dover personas, you can create your own Custom persona.
How to create a Custom persona:
Select Custom from Personas dropdown
Under Modify Titles, create a persona name
Select a title and click Add to add a title pattern to your search and click Save to lock in your changes.
āļø Modify Existing Personas
āļø Modify Existing Personas
How to modify an existing persona:
Click the āļø icon next to an existing persona
Under Modify Titles, you can either remove existing titles by either removing ā or deselecting a title pattern ā or Add a new title pattern to your search
Click Save to lock in your changes.
šÆ Target Current Job Titles only
šÆ Target Current Job Titles only
By default, Dover will target candidates whose job titles match previous roles. To target candidates with current job titles that match your search, you can toggle on Most recent position only in the Advanced dropdown.
ā Exclude Job Titles
ā Exclude Job Titles
You can exclude custom titles from your search. In the Advanced dropdown enter Excluded Custom Titles and press Enter.
š Location
šŗļø Location
šŗļø Location
Target candidates from a specific geographical location
You can target or exclude a certain geographical areas from your search. Please note that if you target New York (US) and United States, Dover will select the larger-sized location. Please note that limiting the location significantly impacts the candidate pool size ā flexibility here can help increase the number of matching candidates.
šļø Seniority
š Years of Experience
š Years of Experience
Total Years of Experience: This scale calculates the total years of career experience candidates have after college. For example, if a candidate had 2 college internships, worked as a consultant for 2 years, and has worked as a product manager for the past 3 years, their total years of experience would be 5 years because their internships were while they were in college.
Specific Years of Experience: This scale calculates the total years of experience in the department specified by the persona. For example, if a candidate had 2 college internships, worked as a consultant for 2 years, has worked as a product manager for the past 3 years, and the target persona selected is āProduct Managerā, their specific years of experience would be 3 years.
Exclude jumpy candidates: This toggle looks at a candidateās past 3 employers. If the toggle is turned on, candidates who averaged < 1.5 years at their 3 most recent companies (not including internships) will be excluded.
Years at current company: Add how many years you'd like the candidate to have at their current company
Title years of experience: Add how many years of experience you'd like the candidate to have in a given job title
š¼ Seniority Level
š¼ Seniority Level
Seniority level looks at a candidateās seniority level indicated by the title pattern.
Specifically:
If a seniority level is selected, candidates will pass if they have the seniority level in either of their past 2 positions.
If a seniority level is unselected, a candidate will be excluded if they have the unselected seniority level in their current position.
The Unknown option captures all candidates who donāt have a seniority title specified. It is recommended to select this seniority level when targeting individual contributors. For example:
āAssociate Software Engineerā = Early career
āSoftware Engineerā = Unknown
āSenior Software Engineerā = Mid career
āPrincipal Software Engineerā = Late career
š¢ Companies
š Company Size
š Company Size
Select what size companies you'd like to target candidates from or exclude candidates from in your search
If a company size bucket is selected, Dover will check both current and previous companies to determine if a company's size criteria passes (excluding internships). To account for changes in company size since a candidate worked at a previous company, Dover will check the size of the company during the time when the
candidate worked there.
Current position only
Toggle this on if you'd like your company size requirements to apply to their current position only.
Add exclusions:
If a company size bucket is selected as an exclusion, Dover will check the current company size of the candidateās most recent company to determine if the candidate should be excluded.
šÆ Company Prestige
šÆ Company Prestige
Dover has a database of rankings for 20k+ companies to assist in grouping the prestige of the companies a candidate has worked for into 5 groups. These rankings are influenced by the following variables: general reputation, strength of investor reputation, strength of schools employees attended, and strength of previous companies that current employees worked at previously. These rankings are often differentiated based on the type of role you are targeting. For example, McKinsey has a high score for operations and finance roles, but has a low score for engineering and design roles. Apple has a high score for marketing and design roles, but has a low score for sales roles.
šÆ Target Companies
šÆ Target Companies
You can target specific companies, or specific lists of companies, for outreach. When company names or lists are input, candidates who work at those companies will still pass the search regardless of whether or not they pass the other variables in the āCompaniesā section (company size, company prestige, industries).
For example, if you specify that you want to target engineers working at a company size < 1000 and the FinTech industry, but you also want to target candidates from Google, you can add Google to Specific Companies and those candidates will still be sourced.
Dover will look at a candidateās past 3 companies when checking for target companies. For example, if Google is listed as a target company, and a candidate has worked at 3 or more companies since their experience at Google, then the candidate will not pass on the target company portion of the calibration.
Save as list
If you enter a long list of target companies, you can use the āSave as listā feature to reuse the list of target companies across other searches
You can create your own under My lists that you can apply to multiple roles
You can select one of Dover lists
Only source from specific companies/lists
Turning on this toggle will only pass candidates who have work experience from the specified target companies and lists.
Current position only
Since Dover looks at a candidateās 3 most recent companies to check for target company experience by default, turning this on will result in Dover only checking candidateās most recent company for target company experience.
Excluded companies
Adding company exclusions will block candidates from passing if their most recent company experience matches any of the listed company exclusions.
Client Level Company Exclusions from your company exclusions here are listed.
š Industries
š Industries
Companies in Doverās database have industry labels associated with them. Selecting industries will tell Dover which industries to target. If an industry is selected, a candidate will pass this criteria if they have more than 1 year of experience working at companies in the specified industry. Selecting multiple industries will pass candidates that have more than 1 year of experience working in any of the specified industries.
Current position only: Toggle on to target candidates who are currently in the industries that you list.
š« Education
š School Prestige
š School Prestige
Dover has a database of rankings for around 3,000 colleges and universities, both US and international, to assist with filtering by prestige of the schools candidates have attended. These rankings are influenced by the following variables: general reputation, U.S. News rankings reports, schools with strong Computer Science programs, schools that place students into well-reputed companies.
Group 1 schools are the most prestigious.
Harvard, Carnegie Mellon, UC Berkeley, University of Waterloo, etc.
Group 2 schools are still highly competitive, but less so than Group 1.
Northwestern, INSEAD, Pomona College, McGill
Group 3 schools are still good schools, but are not as competitive for admissions compared with Group 1 and Group 2 and include many smaller private schools or state sponsored research universities.
Boston University, Georgia Tech, Texas A&M, University of British Colombia
Group 4 schools are lesser known private schools, and less prestigious branches of state university systems.
Denison University, East Carolina University, Fashion Institute of Technology
Group 5 schools include local community colleges with 2-year programs.
Fullerton College, Cerritos College, Napa Valley College
š Target Schools
š Target Schools
You can target specific schools, or specific lists of schools, for outreach. When school names or lists are input, candidates who attended these schools will be given a higher score overall, and will be more likely to pass the search even if they donāt match on a few other ānice to haveā criteria.
Lists
If you enter a long list of target companies, you can use the Save as list feature to reuse the list of target companies across other searches. You can then add Lists to your search.
Only source from specific schools/lists
Turning on this toggle will only pass candidates who have attended the specified target schools and lists.
Exclude specific schools
Adding school exclusions will block candidates from passing if they attended any of the listed schools.
š Degree
š Degree
Field of Study
You can filter based on what candidates studied while attending university.
Each selection has multiple syntax fields within its definition. For example, selecting, selecting āComputer Science - Generalā will include fields of study like computer science, computer engineering, information systems, and computational sciences
Subcategories are indented, and are bucketed under their general category. For example, if āComputer Scienceā is selected, candidates who studied AI/ML will pass. If the subcategory of āComputer Science - Generalā is selected, candidates who studied AI/ML will not pass.
Highest degree earned:
This filter can be used to set a preference for obtaining a Masters or PhD.
This variable indicates the minimum degree earned, so if you select āGraduateā, candidates with Masters and/or PhDs will both pass.
MBAs and JDs are grouped under the āGraduateā category.
š ļø Skills
š Keywords
š Keywords
Adding keywords will enable Dover to scan a candidateās profile to search for mentions of the keywords specified.
Keywords often have "aliases" and "children" keywords associated with them. For example, the keyword āfrontendā has the two aliases ā āfront endā and āfront-endā ā so that all variations will be picked up. The āfrontendā keyword also has many children keywords (e.g. "user experience," "user interface," "web development"), so that all candidates mentioning keywords that relate to frontend development will pass the search.
Adding multiple āmust haveā keywords into the different keyword buckets will use āANDā logic and require both keywords to be present.
Adding multiple āmust haveā keywords into the same keyword bucket will use āORā logic and require at least one keyword to be present.
š” Advanced
š¦ Diversity
š¦ Diversity
This filter allows you to filter by gender or racial diversity. Dover defines non-males as female or non-binary identifying candidates. Dover defines underrepresented minorities as candidates who identify as Black/African American, Hispanic/Latino, or American Indian.
Dover uses a combination of the following factors to determine gender and racial groupings:
self identification (e.g. pronouns in bio)
name recognition
ML to scan profile pictures
ā ļø These filters are not 100% accurate.
āļø Strictness Scale
āļø Strictness Scale
The strictness scale is used to determine the percentage of ānice to haveā variables that need to be present for a candidate to match the search. If you increase the strictness scale, you are decreasing the candidate pool to remove candidates that have the fewest ānice to haveā variables.
A strictness score of 0 will allow candidates don't have any of the ānice to haveā variables, but will still reach out to the candidates who match ānice to haveā variables first.
A strictness score of 10 will only pass candidates that pass all ānice to haveā variables.