Sourcing Autopilot criteria

Dover allows your to completely customize your search parameters to target the ideal candidates for your role.

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Written by Customer Support
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Overview

Dover identifies candidates by analyzing online profiles across a number of sources including LinkedIn, finds contact info about them, and allows you to contact them via Dover's platform. Optimize your search to target the candidates you want by filtering for them based on your must have and nice to have requirements for the job. You will instantly be able to see your searches impact on your candidate pool. A candidate pool is the number of candidates with experience in a given position that meet the criteria for your search.


How to access your Criteria

You can edit the criteria for your job by selecting Sourcing Autopilot and selecting your job from the Active jobs.

  1. Name - create a unique search name

  2. Created By - user who created the search

  3. Last Modified - the last date & time a search was changed

  4. Contacted - number of candidates who were contacted from this search

  5. Remaining - approximate remaining number of candidates in the candidate pool

  6. Status - on/off toggles

  7. Active - if toggled on, the search is active

  8. Automatic Adjustments - if toggled on, Dover will make adjustments to the search to add depth

  9. Outreach Campaign - select an outreach campaign for each search, or click +Add to create a new one

You can run multiple searches simultaneously and filter for the types of candidates you want to target.

How to edit a search:

  1. Click on the search you'd like to change

  2. As you adjust the filters on the left, you will see an impact on the total number of matching candidates for your search.

  3. Once you have made changes to the filters, click Save Search to lock in your changes.

How to create a new search

  1. On the Searches page, click +New Search (note that searches can only be created for existing jobs in Dover).

  2. Complete the New search form by selecting the Job from the dropdown, creating a Search Name, and selecting a Persona

  3. Select whether you would like Dover to guide your search or if you'd like to adjust the filters yourself and click Next.

    ℹ️ If you selected Guide me, you'll fill out some basic information about the role and then be able to fine tune the filters. If you selected Select filters myself, you will not be asked questions and should adjust the filters manually.

  4. When you've made adjustments, click Save Search to lock in your changes.


Criteria Filters

Adjusting each filter will impact the type of candidates who are targeted for your search. To change your search criteria for this role, you can adjust the following filters:

About

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.

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 candidate’s full profile to determine if the candidate is a match.

For example, Dover’s “Frontend Engineer” persona has 20+ title patterns within its definition (e.g. “frontend engineer”, “UI/UX developer,” “software engineer, frontend”) and also scans profiles with generic title patterns like “software engineer” to see if candidates are focused on the frontend.

Dover personas also use “dealbreaker” logic to ensure accuracy. For example, this logic would help to block titles like “Software Engineer Sourcer” from an engineering search.

Titles must be an exact match, so if you input “software engineer," candidate titles such as “software engineering” and “software development engineer” will not match.

About

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 — flexibility here can help increase the number of matching candidates

🖋️ Seniority

📆 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 “Product Manager”, their specific years of experience would be 3 years.

  • Exclude jumpy candidates: This toggle looks at a candidate’s 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

💼 Seniority Level

Seniority level looks at a candidate’s 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 don’t have a seniority title specified. It is recommended to select this seniority level when targeting individual contributors. For example:

    • “Associate Software Engineer” = Early career

    • “Software Engineer” = Unknown

    • “Senior Software Engineer” = Mid career

    • “Principal Software Engineer” = Late career

📈 Company Size

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 candidate’s most recent company to determine if the candidate should be excluded.

💯 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.

🎯 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 “Companies” 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 candidate’s 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.

🏭 Industries

Companies in Dover’s 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.

🔝 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

🎓 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 don’t match on a few other “nice to have” criteria.

📚 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 “Computer Science - General” 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 “Computer Science” is selected, candidates who studied AI/ML will pass. If the subcategory of “Computer Science - General” is selected, candidates who studied AI/ML will not pass.

🛠️ Skills

🔑 Keywords

Adding keywords will enable Dover to scan a candidate’s profile to search for mentions of the keywords specified.

Keywords often have "aliases" and "children" keywords associated with them. For example, the keyword “frontend” has the two aliases — “front end” and “front-end” — so that all variations will be picked up. The “frontend” 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.

🦄 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

⚠️ These filters are not 100% accurate.

⚖️ Strictness Scale

The strictness scale is used to determine the percentage of “nice to have” 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 “nice to have” variables.

  • A strictness score of 0 will allow candidates don't have any of the “nice to have” variables, but will still reach out to the candidates who match “nice to have” variables first.

  • A strictness score of 10 will only pass candidates that pass all “nice to have” variables.


You can also select whether candidate qualities are must-haves or nice-to-haves. Nice to Have are preferences. Must Have are requirements.

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