LinkedIn People Search API

11. LinkedIn search: "VP Sales" + filters
22. Scroll through results (slow loading)
33. Click each profile to verify details
44. Copy name, title, company to spreadsheet
55. Manually check if company fits ICP
66. Find email separately (Hunter.io, etc.)
77. Repeat for "Head of Sales"
88. Repeat for "Sales Director"
99. Remove duplicates
1010. QA the list
11
12Time per qualified contact: 8-12 minutes
13Total time for 100 contacts: 800-1,200 minutes (13-20 hours)
14Cost at $50/hour: $650-1,000

1from linkdapi import LinkdAPI
2
3api = LinkdAPI("your_api_key")
4
5# Search once with all criteria
6results = api.search_profiles(
7    keyword="VP Sales OR Head of Sales OR Sales Director",
8    geo_urn="103644278",  # United States
9    current_company_size=["51-200", "201-500"],
10    industries="6"  # Computer Software
11)
12
13# Get 100+ qualified contacts in seconds
14for profile in results['data']:
15    print(f"{profile['fullName']} - {profile['headline']} at {profile['company']}")
16
17# Time: 30 seconds
18# Cost: $0.30 (100 profiles × $0.003)
19# Quality: Fresh, accurate, structured

1SDR team (5 reps):
2- Manual prospecting: 10 hours/week/rep = 50 hours/week
3- API automation: 30 minutes/week total
4- Time saved: 49.5 hours/week
5- Value at $50/hour: $2,475/week = $128,700/year
6
7API cost: $200-400/month = $2,400-4,800/year
8
9ROI: 2,581-5,362%

1Official API:
2"Give me MY profile data" ✓
3"Search for OTHER people" ✗
4
5Unofficial API:
6"Give me MY profile data" ✗
7"Search for OTHER people" ✓

1# Search by name
2api.search_profiles(keyword="John Smith")
3
4# Search by title
5api.search_profiles(keyword="Software Engineer")
6
7# Search by skills
8api.search_profiles(keyword="Python React AWS")
9
10# Boolean operators
11api.search_profiles(keyword="CEO OR Founder OR Co-Founder")

1# By country
2api.search_profiles(geo_urn="103644278")  # United States
3
4# By city
5api.search_profiles(geo_urn="90009706")  # San Francisco Bay Area
6
7# By region
8api.search_profiles(geo_urn="102095887")  # California

1# By company ID
2api.search_profiles(current_company_id=["1441"])  # OpenAI
3
4# By company size
5api.search_profiles(current_company_size=["51-200", "201-500"])
6
7# Combine with keyword for specific companies
8api.search_profiles(keyword="Google Microsoft Amazon")

1# By industry code
2api.search_profiles(industries="6")  # Computer Software
3
4# Multiple industries
5api.search_profiles(industries="6,3,4")  # Software, Healthcare, Finance

1# English profiles only
2api.search_profiles(profile_language="en")
3
4# Spanish profiles
5api.search_profiles(profile_language="es")

1# People who worked at Google
2api.search_profiles(past_company_id=["1441"])
3
4# Find ex-employees of competitors
5api.search_profiles(past_company_id=["competitor1", "competitor2"])

1# Alumni of specific schools
2api.search_profiles(school_id=["18290"])  # Stanford University
3
4# Find Ivy League graduates
5api.search_profiles(school_id=["ivy_league_schools"])

1# Multiple search terms (OR logic)
2api.search_profiles(keyword="VP Sales OR Head of Sales OR Director Sales")
3
4# Skills and technologies
5api.search_profiles(keyword="React Node.js TypeScript")
6
7# Company names in keyword
8api.search_profiles(keyword="Stripe Square Shopify")

1{
2  "success": true,
3  "data": [
4    {
5      "fullName": "Sarah Chen",
6      "headline": "VP of Sales at Acme Corp",
7      "location": "San Francisco Bay Area",
8      "username": "sarachen",
9      "company": "Acme Corp",
10      "companyId": "12345",
11      "profilePicture": "https://...",
12      "connectionDegree": "2nd",
13      "premium": true
14    }
15  ]
16}

1from linkdapi import LinkdAPI
2
3api = LinkdAPI("your_api_key")
4
5# Simple keyword search
6results = api.search_profiles(keyword="software engineer")
7
8print(f"Found {len(results['data'])} profiles")
9
10for profile in results['data']:
11    print(f"- {profile['fullName']}: {profile['headline']}")

1# Advanced search with multiple criteria
2results = api.search_profiles(
3    keyword="VP Sales OR Head of Sales",
4    geo_urn="103644278",  # United States
5    current_company_size=["51-200", "201-500"],
6    industries="6",  # Computer Software
7    profile_language="en",
8    start=0  # Pagination
9)
10
11# Results are already filtered and relevant
12qualified_prospects = results['data']

1def search_profiles(
2    keyword: str = None,              # Search term (name, title, skills)
3    geo_urn: str = None,              # Geographic location
4    start: int = 0,                   # Pagination (0, 25, 50, etc.)
5    current_company_id: list = None,  # Filter by current company
6    past_company_id: list = None,     # Filter by past companies
7    school_id: list = None,           # Filter by schools attended
8    industries: str = None,           # Industry codes (comma-separated)
9    current_company_size: list = None,# Company size ranges
10    profile_language: str = None      # Language code (en, es, fr, etc.)
11)

1# Get multiple pages of results
2all_results = []
3
4for page in range(4):  # Get 100 results (4 pages × 25 results)
5    results = api.search_profiles(
6        keyword="Product Manager",
7        geo_urn="102221843",  # New York
8        start=page * 25
9    )
10    
11    all_results.extend(results['data'])
12    
13    if len(results['data']) < 25:
14        break  # No more results
15
16print(f"Total results: {len(all_results)}")

1from linkdapi import AsyncLinkdAPI
2import asyncio
3
4async def bulk_search(queries):
5    """
6    Search multiple criteria concurrently
7    """
8    async with AsyncLinkdAPI("your_api_key") as api:
9        # Create tasks for each search
10        tasks = [
11            api.search_profiles(keyword=query, geo_urn="103644278")
12            for query in queries
13        ]
14        
15        # Execute all searches at once
16        results = await asyncio.gather(*tasks)
17        
18        return results
19
20# Search for multiple titles simultaneously
21titles = [
22    "VP Sales",
23    "VP Marketing", 
24    "VP Product",
25    "VP Engineering"
26]
27
28all_results = asyncio.run(bulk_search(titles))
29
30# Process results
31for title, result in zip(titles, all_results):
32    print(f"{title}: {len(result['data'])} people found")

1from linkdapi import LinkdAPI
2
3api = LinkdAPI("your_api_key")
4
5# Your ICP
6ICP = {
7    'titles': ['VP Marketing', 'Head of Marketing', 'Director Marketing', 'CMO'],
8    'company_size': ['51-200', '201-500'],
9    'industry': '6',  # Computer Software
10    'location': '103644278'  # United States
11}

1def find_prospects(icp):
2    """
3    Find all prospects matching ICP
4    """
5    all_prospects = []
6    
7    # Search for each title variant
8    for title in icp['titles']:
9        print(f"Searching for: {title}")
10        
11        results = api.search_profiles(
12            keyword=title,
13            geo_urn=icp['location'],
14            current_company_size=icp['company_size'],
15            industries=icp['industry']
16        )
17        
18        all_prospects.extend(results['data'])
19    
20    # Remove duplicates (same person with multiple title matches)
21    seen = set()
22    unique_prospects = []
23    
24    for prospect in all_prospects:
25        if prospect['username'] not in seen:
26            seen.add(prospect['username'])
27            unique_prospects.append(prospect)
28    
29    return unique_prospects
30
31# Find all matching prospects
32prospects = find_prospects(ICP)
33print(f"Found {len(prospects)} unique prospects")

1def score_prospect(profile):
2    """
3    Score prospect based on fit
4    """
5    score = 0
6    
7    # Title scoring
8    title = profile['headline'].lower()
9    if 'vp' in title or 'vice president' in title:
10        score += 10
11    elif 'head' in title:
12        score += 8
13    elif 'director' in title:
14        score += 6
15    
16    # Company signals
17    if 'saas' in title or 'software' in title:
18        score += 5
19    
20    # Premium account (more engaged on LinkedIn)
21    if profile.get('premium'):
22        score += 3
23    
24    # Connection degree (warmer if 2nd connection)
25    if profile.get('connectionDegree') == '2nd':
26        score += 5
27    
28    return score
29
30# Score all prospects
31for prospect in prospects:
32    prospect['fit_score'] = score_prospect(prospect)
33
34# Sort by score (highest first)
35prospects.sort(key=lambda x: x['fit_score'], reverse=True)
36
37# Top 50 best-fit prospects
38top_prospects = prospects[:50]
39
40print("\nTop 10 Prospects:")
41for i, p in enumerate(top_prospects[:10], 1):
42    print(f"{i}. {p['fullName']} - {p['headline']} (Score: {p['fit_score']})")

1import csv
2
3def export_prospects(prospects, filename='prospects.csv'):
4    """
5    Export prospects to CSV for sales team
6    """
7    with open(filename, 'w', newline='') as f:
8        fieldnames = ['Name', 'Title', 'Company', 'Location', 'LinkedIn', 'Fit Score']
9        writer = csv.DictWriter(f, fieldnames=fieldnames)
10        
11        writer.writeheader()
12        
13        for p in prospects:
14            writer.writerow({
15                'Name': p['fullName'],
16                'Title': p['headline'],
17                'Company': p.get('company', 'N/A'),
18                'Location': p.get('location', 'N/A'),
19                'LinkedIn': f"https://linkedin.com/in/{p['username']}",
20                'Fit Score': p['fit_score']
21            })
22    
23    print(f"Exported {len(prospects)} prospects to {filename}")
24
25# Export top 100 prospects
26export_prospects(top_prospects[:100], 'high_fit_prospects.csv')

1# What you're looking for
2REQUIREMENTS = {
3    'title_keywords': 'Senior Backend Engineer OR Staff Engineer OR Principal Engineer',
4    'skill_keywords': 'Python Kubernetes AWS',
5    'location': '90009706',  # San Francisco Bay Area
6    'company_size': ['51-200', '201-500', '501-1000'],  # Established startups
7    'past_companies': ['1441', '2382910', '791962']  # Google, Meta, Microsoft alumni
8}

1def find_candidates(requirements):
2    """
3    Find candidates matching multiple criteria
4    """
5    candidates = []
6    
7    # Search 1: By title
8    title_results = api.search_profiles(
9        keyword=requirements['title_keywords'],
10        geo_urn=requirements['location'],
11        current_company_size=requirements['company_size']
12    )
13    
14    candidates.extend(title_results['data'])
15    
16    # Search 2: By skills
17    skills_results = api.search_profiles(
18        keyword=requirements['skill_keywords'],
19        geo_urn=requirements['location']
20    )
21    
22    candidates.extend(skills_results['data'])
23    
24    # Search 3: Alumni from top companies
25    alumni_results = api.search_profiles(
26        past_company_id=requirements['past_companies'],
27        geo_urn=requirements['location']
28    )
29    
30    candidates.extend(alumni_results['data'])
31    
32    # Remove duplicates
33    seen = set()
34    unique_candidates = []
35    
36    for candidate in candidates:
37        if candidate['username'] not in seen:
38            seen.add(candidate['username'])
39            unique_candidates.append(candidate)
40    
41    return unique_candidates
42
43candidates = find_candidates(REQUIREMENTS)
44print(f"Found {len(candidates)} potential candidates")

1def qualifies_for_role(profile):
2    """
3    Check if candidate meets minimum requirements
4    """
5    headline = profile['headline'].lower()
6    
7    # Must be an engineer
8    if not any(term in headline for term in ['engineer', 'developer', 'software']):
9        return False
10    
11    # Must be senior+ level
12    seniority_terms = ['senior', 'staff', 'principal', 'lead']
13    if not any(term in headline for term in seniority_terms):
14        return False
15    
16    # Check for relevant skills
17    skills = profile.get('skills', '').lower()
18    required_skills = ['python', 'backend', 'api']
19    
20    skill_match = sum(1 for skill in required_skills if skill in headline or skill in skills)
21    
22    return skill_match >= 2  # At least 2 required skills
23
24# Filter candidates
25qualified_candidates = [c for c in candidates if qualifies_for_role(c)]
26
27print(f"Qualified candidates: {len(qualified_candidates)}")

1def calculate_candidate_score(profile):
2    """
3    Score candidates by desirability
4    """
5    score = 0
6    headline = profile['headline'].lower()
7    
8    # Seniority level
9    if 'principal' in headline or 'staff' in headline:
10        score += 15
11    elif 'senior' in headline:
12        score += 10
13    elif 'lead' in headline:
14        score += 12
15    
16    # Current company quality (if they're at a competitor/good company)
17    company = profile.get('company', '').lower()
18    top_companies = ['google', 'meta', 'amazon', 'microsoft', 'netflix', 'uber']
19    if any(comp in company for comp in top_companies):
20        score += 10
21    
22    # Premium account (active on LinkedIn = easier to reach)
23    if profile.get('premium'):
24        score += 5
25    
26    # 2nd degree connection (mutual connection)
27    if profile.get('connectionDegree') == '2nd':
28        score += 8
29    
30    # Tech stack matches
31    desired_tech = ['python', 'kubernetes', 'aws', 'docker', 'microservices']
32    tech_matches = sum(1 for tech in desired_tech if tech in headline)
33    score += tech_matches * 3
34    
35    return score
36
37# Score all candidates
38for candidate in qualified_candidates:
39    candidate['recruiter_score'] = calculate_candidate_score(candidate)
40
41# Sort by score
42qualified_candidates.sort(key=lambda x: x['recruiter_score'], reverse=True)
43
44# Top 20 candidates
45top_candidates = qualified_candidates[:20]
46
47print("\nTop 10 Candidates to Contact:")
48for i, c in enumerate(top_candidates[:10], 1):
49    print(f"{i}. {c['fullName']} - {c['headline']}")
50    print(f"   Company: {c.get('company', 'N/A')} | Score: {c['recruiter_score']}")

1import json
2from datetime import datetime
3
4def save_candidate_pipeline(candidates):
5    """
6    Save candidates to recruiting pipeline
7    """
8    pipeline = []
9    
10    for candidate in candidates:
11        pipeline.append({
12            'name': candidate['fullName'],
13            'headline': candidate['headline'],
14            'company': candidate.get('company'),
15            'location': candidate.get('location'),
16            'linkedin_url': f"https://linkedin.com/in/{candidate['username']}",
17            'score': candidate['recruiter_score'],
18            'status': 'to_contact',
19            'added_date': datetime.now().isoformat(),
20            'notes': ''
21        })
22    
23    with open('candidate_pipeline.json', 'w') as f:
24        json.dump(pipeline, f, indent=2)
25    
26    print(f"Saved {len(pipeline)} candidates to pipeline")
27
28save_candidate_pipeline(top_candidates)

1from flask import Flask, request, jsonify, render_template
2from linkdapi import LinkdAPI
3
4app = Flask(__name__)
5api = LinkdAPI("your_api_key")
6
7@app.route('/')
8def home():
9    return render_template('search.html')
10
11@app.route('/api/search', methods=['POST'])
12def search():
13    """
14    API endpoint for LinkedIn search
15    """
16    data = request.json
17    
18    # Extract search parameters
19    keyword = data.get('keyword', '')
20    location = data.get('location', '')
21    company_size = data.get('company_size', [])
22    
23    # Search LinkedIn
24    results = api.search_profiles(
25        keyword=keyword,
26        geo_urn=location if location else None,
27        current_company_size=company_size if company_size else None
28    )
29    
30    # Format results
31    profiles = []
32    for profile in results['data']:
33        profiles.append({
34            'name': profile['fullName'],
35            'headline': profile['headline'],
36            'company': profile.get('company', 'N/A'),
37            'location': profile.get('location', 'N/A'),
38            'linkedin_url': f"https://linkedin.com/in/{profile['username']}",
39            'image': profile.get('profilePicture')
40        })
41    
42    return jsonify({
43        'success': True,
44        'count': len(profiles),
45        'profiles': profiles
46    })
47
48if __name__ == '__main__':
49    app.run(debug=True)

1<!DOCTYPE html>
2<html>
3<head>
4    <title>LinkedIn Search Tool</title>
5    <style>
6        body { font-family: Arial, sans-serif; max-width: 1200px; margin: 0 auto; padding: 20px; }
7        .search-box { background: #f5f5f5; padding: 20px; border-radius: 8px; }
8        input, select { padding: 10px; margin: 5px; width: 300px; }
9        button { padding: 10px 20px; background: #0077b5; color: white; border: none; cursor: pointer; }
10        .results { margin-top: 20px; }
11        .profile-card { border: 1px solid #ddd; padding: 15px; margin: 10px 0; border-radius: 5px; }
12        .profile-card img { width: 60px; height: 60px; border-radius: 50%; float: left; margin-right: 15px; }
13    </style>
14</head>
15<body>
16    <h1>🔍 LinkedIn Search Tool</h1>
17    
18    <div class="search-box">
19        <input type="text" id="keyword" placeholder="Job title, skills, or name">
20        <select id="location">
21            <option value="">All Locations</option>
22            <option value="103644278">United States</option>
23            <option value="90009706">San Francisco Bay Area</option>
24            <option value="102221843">New York City</option>
25        </select>
26        <button onclick="search()">Search</button>
27    </div>
28    
29    <div id="results" class="results"></div>
30    
31    <script>
32        async function search() {
33            const keyword = document.getElementById('keyword').value;
34            const location = document.getElementById('location').value;
35            
36            const response = await fetch('/api/search', {
37                method: 'POST',
38                headers: {'Content-Type': 'application/json'},
39                body: JSON.stringify({keyword, location})
40            });
41            
42            const data = await response.json();
43            displayResults(data.profiles);
44        }
45        
46        function displayResults(profiles) {
47            const resultsDiv = document.getElementById('results');
48            resultsDiv.innerHTML = `<h3>Found ${profiles.length} profiles</h3>`;
49            
50            profiles.forEach(profile => {
51                resultsDiv.innerHTML += `
52                    <div class="profile-card">
53                        <img src="${profile.image}" alt="${profile.name}">
54                        <h4>${profile.name}</h4>
55                        <p>${profile.headline}</p>
56                        <p>📍 ${profile.location}</p>
57                        <a href="${profile.linkedin_url}" target="_blank">View Profile</a>
58                    </div>
59                `;
60            });
61        }
62    </script>
63</body>
64</html>