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Employee Count Filter

The employee count filter lets you target companies of a specific size. Company size has a direct impact on how they hire, what kind of recruitment support they need, and how you should approach them. Filtering by employee count helps you focus on the segment where your recruitment services deliver the most value — whether that is agile startups, growing mid-market companies, or established enterprises.

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Why Company Size Matters

Different company sizes have fundamentally different recruitment needs and behaviors:

Small Companies (1-50)

Small companies and startups often do not have dedicated HR teams. They rely heavily on external recruiters for critical hires. However, they typically have fewer openings and smaller budgets. The relationship is often directly with the founder or a hiring manager, which means faster decisions but also more price sensitivity.

Mid-Size Companies (51-500)

Mid-size companies are the sweet spot for many recruitment agencies. They have regular hiring needs, budgets for external recruitment, but may not have fully built-out internal recruitment teams. They often need help with specialized roles and can become long-term repeat clients as they grow.

Large Companies (501-5,000)

Large companies usually have internal recruitment teams but still use external recruiters for hard-to-fill roles, volume hiring, and specialized positions. Engagements tend to be more structured with preferred supplier lists and formal processes. The contract value is higher but the sales cycle is longer.

Enterprise (5,000+)

Enterprise companies typically work with a small number of preferred recruitment partners through formal vendor programs. Getting in the door requires a strong track record and often a formal RFP process. However, these relationships can be highly valuable long-term with consistent, high-volume placement opportunities.

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How Employee Data Is Sourced

Employee count data comes from LinkedIn company profiles. When Recruitier enriches a company, it pulls the employee count from the company’s LinkedIn page. This number represents the count of LinkedIn members who list the company as their employer.
LinkedIn employee counts represent the number of people who have listed the company as their employer on LinkedIn. The actual company size may differ — especially for blue-collar industries where LinkedIn adoption is lower. However, LinkedIn counts are a reliable approximation for most white-collar companies and are the best publicly available data source for company sizing.

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Limitations to Be Aware Of

  • Some companies have no employee count data — If the LinkedIn profile could not be found or does not include this information, the field will be empty. These companies still appear in unfiltered results but cannot be matched against a size filter.
  • Counts may be approximate — LinkedIn shows ranges on the public profile (e.g., “51-200 employees”), but the actual number stored is the specific count from the API when available.
  • Counts may lag — If a company recently grew or shrank significantly, the LinkedIn count may not reflect the most current situation. Fast-growing startups may have substantially more employees than their LinkedIn count suggests.
  • Industry bias — Companies in technology and professional services tend to have accurate LinkedIn counts. Companies in manufacturing, agriculture, or retail may have significantly more employees than their LinkedIn count shows.

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Setting Employee Count Ranges

The filter provides minimum and maximum fields that you can set independently:
1

Set Minimum Employee Count

Enter a minimum number to exclude companies smaller than this. For example, set to 50 to exclude very small companies and focus on established organizations.
2

Set Maximum Employee Count

Enter a maximum number to exclude companies larger than this. For example, set to 500 to focus on mid-size companies and avoid enterprises with complex vendor management processes.
3

Or Set Just One Bound

You do not need to set both. Setting only a minimum shows all companies above that size. Setting only a maximum shows all companies below that size. This gives you flexibility to define open-ended ranges.

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Common Size Ranges

SegmentMinMaxTypical Characteristics
Startups120Very early stage, founder-led hiring, every hire is critical
Small150Limited HR, high-impact individual hires, fast decision-making
SMB50200Growing teams, regular hiring needs, beginning to formalize processes
Mid-Market2001,000Established companies with structured hiring, may have internal recruiters
Large1,0005,000Internal TA teams, need specialists and hard-to-fill roles
Enterprise5,000—Formal vendor programs, RPO potential, high-volume placement

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Combining Employee Count with Other Filters

Employee count is especially useful when combined with other filters to target very specific segments:

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Size + Industry

Find mid-size IT companies (100-500 employees) that are hiring. These companies are large enough to have regular recruitment needs but small enough that they may not have a full internal TA team — the ideal combination for an external recruiter.

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Size + Hotness Score

Filter for small companies (under 100 employees) and sort by hotness score. Small companies with high hotness scores are growing rapidly relative to their current size — these are often startups in a growth phase that urgently need help filling multiple roles and may become long-term clients.

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Size + Skills

Find companies with 200-1,000 employees that are posting DevOps jobs. This targets established companies large enough to have dedicated infrastructure teams but still likely to need external recruitment support for specialized roles that are hard to fill internally.

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Size + Location

Find small companies (under 50 employees) within 20 km of your office. This targets the local startup ecosystem where your geographic presence and personal relationships matter most. Small companies near you are the easiest first meetings to set up.
The hotness score already factors in company size (it is calculated as jobs divided by employees). But combining an explicit employee count filter with hotness score lets you focus on a specific segment while still prioritizing the most actively hiring companies within that segment. This is more targeted than using hotness score alone.

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How Employee Count Relates to Hotness Score

The hotness score is calculated as: 
hotness_score = active_job_count / employee_count
This means:
  • A company with 10 jobs and 50 employees has a hotness score of 0.20 (20%).
  • A company with 10 jobs and 500 employees has a hotness score of 0.02 (2%).
Both companies have the same number of open roles, but the smaller company is hiring at 10x the relative intensity. The hotness score captures this difference automatically. When you filter by employee count, you are effectively controlling one variable in this equation. Filtering for small companies and sorting by hotness score will surface the fastest-growing startups. Filtering for large companies and sorting by job count will surface the enterprises with the most open roles in absolute terms.
Companies without employee data receive a hotness score of zero. This means they sort to the bottom when sorted by hotness score but still appear in the results. If you want to focus only on companies with known sizes, set a minimum employee count of 1 to exclude companies with NULL employee data.

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Aggregator Detection

Recruitier automatically detects and filters out companies that appear to be job aggregators rather than real employers. This detection uses employee count as one of its signals:
  • Pattern 1: Small company, too many jobs — Companies with very few employees but a disproportionately large number of job postings are flagged as potential aggregators. A legitimate 5-person company is unlikely to have 40 open positions.
  • Pattern 2: Unknown size, too many jobs — Companies with unknown employee counts but a very high number of job postings are also flagged. Without employee data to normalize, an excessive job count is suspicious.
  • Pattern 3: Name-based detection — Company names containing keywords like “vacatures”, “jobs”, or “recruitment” add additional signals. These keywords frequently appear in the names of job aggregator websites.
All three patterns are OR-combined — matching any one is sufficient for exclusion. This filtering happens automatically — you do not need to configure anything. It ensures that the companies in your search results are genuine employers, not job boards or aggregator sites.
If you notice a company that seems to be an aggregator appearing in results, or a legitimate company missing because it was incorrectly flagged, please contact support. The detection thresholds are tuned to minimize false positives, but edge cases can occur with unusual business models.

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Advanced

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How Employee Count Filtering Works Under the Hood

The employee count filter applies a simple BETWEEN range on the employee_count column of the GlobalCompany entity. When only a minimum is set, it becomes employee_count >= min. When only a maximum is set, it becomes employee_count <= max. When both are set, it becomes employee_count BETWEEN min AND max. Companies with NULL employee count are excluded when any employee count filter is applied, because a NULL value cannot satisfy a numeric comparison. This is an implicit side effect of the filter — you do not need to explicitly exclude them.

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Aggregator Detection in Detail

The aggregator detection operates through three OR-combined conditions applied to every search query:
  1. employee_count <= MAX_EMPLOYEES_THRESHOLD AND job_count > SMALL_COMPANY_JOB_THRESHOLD
  2. employee_count IS NULL AND job_count > UNKNOWN_EMPLOYEE_JOB_THRESHOLD
  3. LOWER(company_name) LIKE ANY (AGGREGATOR_NAME_KEYWORDS)
These thresholds are configurable in the application settings. The current values are tuned based on analysis of the Dutch job market to minimize false positives while catching the most common aggregator patterns.

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How Employee Count Affects the Hotness Score

The hotness score formula divides job count by employee count:
  • If employee_count is NULL: hotness score = 0
  • If employee_count is 0: hotness score = 0
  • Otherwise: hotness score = job_count / employee_count
This means companies without employee data are penalized in hotness score ranking but not excluded from results. Setting a minimum employee count of 1 effectively removes these companies from your results entirely.

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Connection to Other Features

  • Hiring Trends: Company size provides context for interpreting hiring trends. A 50-person company posting 10 jobs per month is undergoing massive growth. A 5,000-person company posting the same number is experiencing normal turnover.
  • Decision Maker Strategy: Company size determines the decision maker discovery strategy. Small companies (under 10 employees) have all employees fetched and ranked. Larger companies use a targeted search focused on HR and leadership roles.
  • Pipeline Strategy: Your outreach approach should differ by company size. The company detail page shows employee count prominently, reminding you to adapt your pitch.

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Power User Tips

  • Filter to min 1 employee for cleaner hotness scores: This removes companies with NULL employee data that always get a zero hotness score, making the hotness ranking more meaningful.
  • Use size as a proxy for decision speed: Smaller companies tend to make faster hiring and vendor decisions. If you need to close deals quickly, filter for smaller companies.
  • Watch for the size-hotness interaction: A company with 10 employees and 5 jobs has a 50% hotness score. This is extremely high but may just mean it is a growing startup. A company with 500 employees and the same hotness score (250 jobs) is a massive hiring operation. Employee count helps you interpret what the hotness score means in practice.
  • Consider combining with industry: A 200-person IT company has very different recruitment needs than a 200-person manufacturing company. Size alone does not tell you enough — combine it with industry for a complete picture.