Leads Finder by Scalelist

Find targeted B2B lead profiles matching job titles, departments, career levels, past roles, skills, locations, industries, and company criteria — powered by Scalelist without leaving your Apify workflow.

Search over millions of B2B profiles and export clean, verified firmographic and professional profile data directly to the Apify Dataset.

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How it works

1. Set Number of leads to extract — how many leads you want back.
2. Narrow your audience using any combination of the 46 filter fields, grouped on the input form into Job Title & Role, Experience, Past Roles, Company, Company Size, Industry, Skills & Background, Keywords, Signals and Person Location. Most filters have an Exclude twin that drops anyone who matches.

The Actor fetches matching profiles progressively and writes them straight to your Apify dataset.

You are charged per lead delivered, via Apify Pay-Per-Event (PPE). If fewer leads match than you asked for, you are only charged for the leads actually delivered — asking for 10,000 when only 100 match costs you 100.

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Input

Field	Form label	Section	Type	Description
maxResults	Number of leads to extract	—	integer	How many leads to return (default: 100, max: 10,000)
jobTitles	Job Titles	Job Title & Role	string[]	Job titles to match (OR logic), e.g. ["VP Sales", "Chief Technology Officer"]
excludeJobTitles	Exclude Job Titles	Job Title & Role	string[]	Job titles to exclude from results
jobFunctions	Job Functions	Job Title & Role	string[]	Functional departments, e.g. ["Sales", "Engineering", "Marketing"]
excludeJobFunctions	Exclude Job Functions	Job Title & Role	string[]	Functions to exclude (current role)
seniority	Seniority Levels	Job Title & Role	string[]	Career levels, e.g. ["CXO", "Vice President", "Director"]
excludeSeniority	Exclude Seniority Levels	Job Title & Role	string[]	Career levels to exclude (current role)
yearsOfExperienceMin / Max	Years of Experience Min / Max	Experience	integer	Total years of work experience
yearsInCurrentRoleMin / Max	Years in Current Role Min / Max	Experience	integer	Years at the current employer
recentlyChangedJobs	Recently Changed Jobs	Experience	boolean	Only people who started their current role in about the last 3 months
pastCompanies	Past Companies	Past Roles	string[]	Companies the person worked at before, e.g. ["Google"]
excludePastCompanies	Exclude Past Companies	Past Roles	string[]	Drop anyone who worked at these companies before
pastJobTitles	Past Job Titles	Past Roles	string[]	Job titles held in a past role, e.g. ["Founder"]
excludePastJobTitles	Exclude Past Job Titles	Past Roles	string[]	Drop anyone who held these titles in a past role
pastSeniority	Past Seniority Levels	Past Roles	string[]	Career level of a past role
excludePastSeniority	Exclude Past Seniority Levels	Past Roles	string[]	Drop anyone who held these levels in a past role
companies	Company Name	Company	string[]	Names of companies to include (OR logic), e.g. ["Google", "Stripe"]
excludeCompanies	Exclude Company Name	Company	string[]	Names of companies to exclude from results
companyDomains	Company Domains	Company	string[]	Company website domains to include, e.g. ["stripe.com"]
excludeCompanyDomains	Exclude Company Domains	Company	string[]	Company website domains to exclude
companyHqCountries	Company HQ Countries	Company	string[]	Where the current employer has its headquarters, e.g. ["Germany", "USA"]
excludeCompanyHqCountries	Exclude Company HQ Countries	Company	string[]	HQ countries to exclude
companyTypes	Company Types	Company	string[]	Employer type, e.g. ["Public Company", "Privately Held"]
companySizeMin	Company Size Min	Company Size	integer	Minimum employee count (optional)
companySizeMax	Company Size Max	Company Size	integer	Maximum employee count (optional)
industries	Industries	Industry	string[]	Industries to match, e.g. ["Computer Software", "Financial Services"]
excludeIndustries	Exclude Industries	Industry	string[]	Industries to exclude (current employer)
skills / excludeSkills	Skills / Exclude Skills	Skills & Background	string[]	Skills listed on the profile, e.g. ["Salesforce", "Python"]
languages / excludeLanguages	Languages / Exclude Languages	Skills & Background	string[]	Languages the person speaks, e.g. ["French"]
schools / excludeSchools	Schools / Exclude Schools	Skills & Background	string[]	Schools or universities attended, e.g. ["Stanford"]
certifications / excludeCertifications	Certifications / Exclude Certifications	Skills & Background	string[]	Certifications held, e.g. ["PMP"]
keywords	Keywords	Keywords	string[]	Free-text keywords (OR logic) matched on headline, bio or company name
excludeKeywords	Exclude Keywords	Keywords	string[]	Drop anyone whose profile contains these words
openTo	Open To	Signals	string[]	Profile signals: hiring, open_to_work, volunteering
minFollowers	Minimum LinkedIn Followers	Signals	integer	Only people with at least this many followers
countries	Countries	Person Location	string[]	Countries to match, e.g. ["United States", "United Kingdom"]
states	States / Regions	Person Location	string[]	States / provinces to match, e.g. ["California", "Ontario"]
cities	Cities	Person Location	string[]	Cities to match, e.g. ["San Francisco", "London", "Singapore"]
excludeCountries / excludeStates / excludeCities	Exclude Countries / States / Cities	Person Location	string[]	Locations to exclude, e.g. United States except California

companies matches on the company's name; use companyDomains to match on its website instead.

Past roles go together. pastCompanies, pastJobTitles and pastSeniority must all match the same past role: pastCompanies: ["Google"] + pastSeniority: ["Vice President"] = people who were a VP at Google.

Exclusions are strict. An exclusion drops anyone with any match, e.g. excludeSkills: ["Java"] drops everyone who lists Java, even with other skills.

Bad values stop the run at once. If the search engine cannot read a filter (for example an industry name that matches no industry), the run fails with the reason in the log. You are not charged.

keyword (one free-text word) is still accepted from saved inputs and the API, but the form now uses keywords.

Example input: Search for Software Engineering VPs & Directors

{
  "maxResults": 100,
  "jobTitles": ["VP Engineering", "Director of Engineering"],
  "jobFunctions": ["Engineering"],
  "seniority": ["Vice President", "Director"],
  "industries": ["Computer Software", "Internet"],
  "companySizeMin": 50,
  "companySizeMax": 500,
  "countries": ["United States"]
}

Example input: Target named accounts

{
  "maxResults": 500,
  "companies": ["Stripe", "Ramp", "Brex"],
  "seniority": ["CXO", "Vice President"],
  "countries": ["United States"]
}

Example input: Ex-founders now in sales, outside staffing

{
  "maxResults": 200,
  "jobFunctions": ["Sales"],
  "pastJobTitles": ["Founder", "Co-Founder"],
  "excludeIndustries": ["Staffing & Recruiting"],
  "yearsInCurrentRoleMax": 2,
  "countries": ["United States"],
  "excludeStates": ["California"]
}

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Output

Every delivered lead is written to the Apify Dataset with the following fields:

Field	Type	Description
full_name	string	Lead's full name
first_name	string	First name
last_name	string	Last name
job_title	string	Current job title
seniority	string	Seniority level (CXO, Vice President, Director, Experienced Manager, etc.)
job_function	string	Functional department
company_name	string	Current employer
company_domain	string	Company website domain
company_size	string	Company employee count range
industry	string	Industry category
city	string	City
state	string	State or region
country	string	Country
linkedin_url	string	Public LinkedIn profile URL
company_linkedin_url	string	Company LinkedIn page URL
profile_picture_url	string	Profile image URL (if available)

Example output row

{
  "full_name": "Jane Doe",
  "first_name": "Jane",
  "last_name": "Doe",
  "job_title": "VP of Engineering",
  "seniority": "Vice President",
  "job_function": "Engineering",
  "company_name": "Acme Cloud Inc.",
  "company_domain": "acmecloud.com",
  "company_linkedin_url": "https://www.linkedin.com/company/acme-cloud",
  "company_size": "51-200",
  "industry": "Computer Software",
  "city": "San Francisco",
  "state": "California",
  "country": "United States",
  "linkedin_url": "https://www.linkedin.com/in/janedoe",
  "profile_picture_url": "https://media.licdn.com/dms/image/..."
}

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Enriching your leads

This Actor finds and extracts lead profiles. It does not return emails or phone numbers — run the results through the enrichment Actors for those:

• Email Finder — verified business emails
• Phone Finder — mobile numbers
• Domain Finder — all emails at a company, from its domain

Output is snake_case (first_name, company_domain, linkedin_url) so it maps directly onto what those Actors expect.

Error handling

Situation	Behaviour
Rate limit (429)	Exponential backoff, up to 5 automatic retries
Invalid filter (400 / 422)	The run fails at once with the reason in the log; no retries, no charge
Fewer matches than requested	Search ends when results are exhausted; you are charged only for leads delivered
Spend cap reached	Only the leads that could be charged are delivered, then the Actor stops cleanly and flushes all saved dataset rows
Duplicate profiles	Deduplicated within the run by LinkedIn URL; duplicates are never charged

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Other Scalelist Actors

• Email Finder — find your leads' verified email
• Phone Finder — find your leads' mobile number
• Domain Finder — find leads and their emails from a company domain

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Support

• Scalelist Website
• Scalelist Support: hello@scalelist.com