LinkedIn Metrics Dataset

Access LinkedIn employee counts and follower metrics as alternative data signals. Track workforce growth and company popularity trends that may precede stock price movements.

What’s Included

The LinkedIn Metrics dataset provides:

Employee Count: Current number of employees on LinkedIn
Employee Growth: Month-over-month and year-over-year changes
Follower Count: Company page followers
Follower Growth: Trending popularity metrics
Job Count: Open job listings (when available)
Historical Data: Track workforce trends over time
Weekly Updates: Fresh data every week

Coverage

Coverage	Details
Markets	S&P 500, NASDAQ, NYSE
Metrics	Employees, followers, jobs, growth rates
Update Frequency	Weekly
Historical Data	2+ years

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.linkedin_data.ticker("META", as_dataframe=True)
print(df)

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.finbrain.tech"

headers = {"Authorization": f"Bearer {API_KEY}"}

response = requests.get(f"{BASE_URL}/v2/linkedin/META", headers=headers)
result = response.json()

# Access LinkedIn data from the response envelope
for entry in result["data"][:5]:
    print(f"{entry['date']}: {entry['employeeCount']} employees | {entry['followerCount']} followers")

For complete code examples in Python, JavaScript, C++, Rust, and cURL, see the API Reference.

Visualization

Plot LinkedIn metrics with the built-in SDK chart:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# One-line interactive chart (employee bars + follower line)
fb.plot.linkedin("AAPL")

LinkedIn Metrics Chart — AAPL LinkedIn employee and follower trends over time

Interpreting LinkedIn Metrics

Employee Growth Signals

YoY Growth	Interpretation	Signal
> 20%	Rapid expansion	Strong growth phase
10% - 20%	Healthy growth	Positive outlook
0% - 10%	Moderate growth	Stable operations
-10% - 0%	Slight contraction	Caution
< -10%	Significant layoffs	Potential distress

Follower Growth Signals

MoM Growth	Interpretation
> 3%	Viral growth / major news
1% - 3%	Above average interest
0% - 1%	Normal growth
< 0%	Declining interest

Use Cases

Workforce Trend Scanner

Scan for companies with significant workforce changes:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def scan_workforce_changes(tickers):
    """Find companies with significant workforce changes"""
    results = []

    for symbol in tickers:
        try:
            df = fb.linkedin_data.ticker(symbol, as_dataframe=True)

            if df.empty or len(df) < 30:
                continue

            # Compare current to 30 days ago
            latest_employees = df["employeeCount"].iloc[0]
            older_employees = df["employeeCount"].iloc[29]

            change = ((latest_employees - older_employees) / older_employees) * 100

            results.append({
                "symbol": symbol,
                "employees": latest_employees,
                "change_30d": change
            })
        except Exception:
            continue

    # Sort by change
    return sorted(results, key=lambda x: x["change_30d"], reverse=True)

tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA", "NFLX"]
changes = scan_workforce_changes(tickers)

print("Workforce Changes (30-day):")
for c in changes:
    trend = "hiring" if c["change_30d"] > 0 else "declining"
    print(f"  {c['symbol']}: {c['employees']:,} employees ({c['change_30d']:+.1f}% - {trend})")

Growth Momentum Indicator

Create a composite growth score:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def calculate_growth_momentum(symbol):
    """Calculate growth momentum from LinkedIn data"""
    df = fb.linkedin_data.ticker(symbol, as_dataframe=True)

    if df.empty or len(df) < 7:
        return None

    # Calculate weekly growth rates
    emp_growth = ((df["employeeCount"].iloc[0] - df["employeeCount"].iloc[6]) / df["employeeCount"].iloc[6]) * 100
    fol_growth = ((df["followerCount"].iloc[0] - df["followerCount"].iloc[6]) / df["followerCount"].iloc[6]) * 100

    # Weighted composite score
    momentum = emp_growth * 0.6 + fol_growth * 0.4

    return {
        "symbol": symbol,
        "momentum_score": momentum,
        "employee_growth": emp_growth,
        "follower_growth": fol_growth,
        "signal": "bullish" if momentum > 1 else "bearish" if momentum < -1 else "neutral"
    }

momentum = calculate_growth_momentum("NVDA")
print(f"{momentum['symbol']}: {momentum['signal']} (Score: {momentum['momentum_score']:.2f})")

Tech Sector Comparison

Compare workforce trends across tech sector:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def compare_tech_workforce():
    """Compare workforce metrics across major tech companies"""
    tech_tickers = {
        "AAPL": "Apple",
        "MSFT": "Microsoft",
        "GOOGL": "Alphabet",
        "AMZN": "Amazon",
        "META": "Meta",
        "NVDA": "NVIDIA",
        "TSLA": "Tesla"
    }

    results = []

    for symbol, name in tech_tickers.items():
        try:
            df = fb.linkedin_data.ticker(symbol, as_dataframe=True)

            if df.empty:
                continue

            results.append({
                "company": name,
                "symbol": symbol,
                "employees": df["employeeCount"].iloc[0],
                "followers": df["followerCount"].iloc[0]
            })
        except Exception:
            continue

    # Sort by employee count
    return sorted(results, key=lambda x: x["employees"], reverse=True)

comparison = compare_tech_workforce()
print("Tech Workforce Comparison:")
print("-" * 60)
for c in comparison:
    print(f"{c['company']:12} | {c['employees']:>10,} employees | {c['followers']:>12,} followers")

Detect Hiring Acceleration

Find companies that are accelerating hiring:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def detect_hiring_acceleration(symbol, lookback_days=30):
    """Detect if a company is accelerating hiring"""
    df = fb.linkedin_data.ticker(symbol, as_dataframe=True)

    if df.empty or len(df) < lookback_days:
        return None

    # Calculate growth rate for recent period vs older period
    emp = df["employeeCount"]
    recent_start = emp.iloc[0]
    recent_end = emp.iloc[lookback_days // 2 - 1]
    older_start = emp.iloc[lookback_days // 2]
    older_end = emp.iloc[lookback_days - 1]

    recent_growth = ((recent_start - recent_end) / recent_end) * 100
    older_growth = ((older_start - older_end) / older_end) * 100

    acceleration = recent_growth - older_growth

    return {
        "symbol": symbol,
        "recent_growth": recent_growth,
        "older_growth": older_growth,
        "acceleration": acceleration,
        "signal": "accelerating" if acceleration > 0.5 else "decelerating" if acceleration < -0.5 else "stable"
    }

result = detect_hiring_acceleration("NVDA")
print(f"Hiring trend: {result['signal']} (Acceleration: {result['acceleration']:.2f}%)")

LinkedIn Data API Reference - Endpoint details, parameters, and response schema
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