Description: This dataset contains simulated employee records for a fictional company. The dataset was generated using the Python Faker library to create realistic but fake data. The dataset includes the following fields for each employee:

Employee ID: A unique identifier for each employee (integer). Name: A randomly generated full name (string). Job title: A randomly generated job title (string). Department: A randomly selected department from a predefined list (HR, Marketing, Sales, IT, or Finance) (string). Email: A randomly generated email address (string). Phone number: A randomly generated phone number (string). Date of hiring: A randomly generated hiring date within the last 10 years (date). Salary: A randomly generated salary value between 30,000 and 150,000 (decimal). Please note that this dataset is for demonstration and testing purposes only. The data is entirely fictional and should not be used for any decision-making or analysis.

🏢 Synthetic Company HR, Org, and Compensation Dataset (2014–2025)

This dataset simulates a global company’s workforce with thousands of fictional employees working across multiple countries and 25+ departments.
It was generated entirely using the Faker library and structured to mirror realistic corporate data — perfect for practicing data analysis, visualization, and machine learning in an HR or organizational context.

The dataset includes demographic details, departments, locations, promotions, annual salaries, and hierarchical manager–employee relationships, enabling both cross-sectional and time-series analysis.

📁 Dataset Overview

Each table captures a key dimension of the corporate structure:

• employees.csv — Core demographic and organizational attributes
• departments.csv — Department names and categories
• locations.csv — Global office locations with cost-of-living indices
• promotions.csv — Career progression events over time
• salaries_annual.csv — Yearly compensation history (2014–2025)
• org_edges.csv — Manager–employee relationships for org chart visualization

💡 Example Use Cases

• HR analytics and workforce demographics
• Salary modeling and pay equity studies
• Career growth and promotion trend analysis
• Organizational network visualization
• Synthetic data for ML model testing and teaching

⚙️ Generation: All records were generated using the Faker Python library and follow probabilistic distributions for realism.
📅 Period Covered: 2014–2025
🔢 Size: ~3,500 employees, 25+ departments, 20 countries
🧠 Note: This dataset is fully synthetic — all entities are fictional and any resemblance to real individuals is purely coincidental.

The 10,000 Worlds Employee Dataset is a comprehensive dataset designed for analyzing workforce trends, employee performance, and organizational dynamics within a large-scale company setting. This dataset contains information on 10,000 employees, spanning various departments, roles, and experience levels. It is ideal for research in human resource analytics, machine learning applications in employee retention, performance prediction, and diversity analysis.

Key Features of the Dataset: Employee Demographics:

Age, gender, ethnicity Education level, degree specialization Years of experience Employment Details:

Department (e.g., HR, Engineering, Marketing) Job title and seniority level Employment type (full-time, part-time, contract) Performance & Productivity Metrics:

Annual performance ratings Work hours, overtime details Training programs attended Compensation & Benefits:

Salary, bonuses, stock options Benefits (healthcare, pension plans, remote work options) Employee Engagement & Retention:

Job satisfaction scores Attrition and turnover rates Promotion history and career growth Workplace Environment Factors:

Team collaboration metrics Employee feedback and survey results Work-life balance indicators Use Cases: HR Analytics: Identifying patterns in employee satisfaction, retention, and performance. Predictive Modeling: Forecasting attrition risks and promotion likelihoods. Diversity & Inclusion Analysis: Understanding representation across departments. Compensation Benchmarking: Comparing salaries and benefits within and across industries. This dataset is highly valuable for data scientists, HR professionals, and business analysts looking to gain insights into workforce dynamics and improve organizational strategies.

Would you like any additional details or a sample schema for the dataset?

Number of active employees, aggregating information from multiple data providers. This series is based on firm-level payroll data from Paychex and Intuit, worker-level data on employment and earnings from Earnin, and firm-level timesheet data from Kronos. This data is compiled by Opportunity Insights. Data notes from Opportunity Insights: Data Source: Paychex, Intuit, Earnin, Kronos Update Frequency: Weekly Date Range: January 15th 2020 until the most recent date available. The most recent date available for the full series depends on the combination of Paychex, Intuit and Earnin data. We extend the national trend of aggregate employment and employment by income quartile by using Kronos timecard data and Paychex data for workers paid on a weekly paycycle to forecast beyond the end of the Paychex, Intuit and Earnin data. Data Frequency: Daily, presented as a 7-day moving average Indexing Period: January 4th - January 31st Indexing Type: Change relative to the January 2020 index period, not seasonally adjusted. More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

Enhance your workforce insights with comprehensive Employee Dataset, designed to help businesses improve recruitment strategies, track employment trends, and optimize workforce planning. This dataset provides structured and reliable employee data for HR professionals, recruiters, and analysts.

Dataset Features

Employee Profiles: Access detailed public employee data, including names, job titles, industries, locations, experience, and skills. Ideal for talent acquisition, workforce analytics, and competitive hiring strategies. Company Employment Data: Gain insights into company workforce distribution, employee tenure, hiring trends, and organizational structures. Useful for market research, HR benchmarking, and business intelligence. Job Listings & Open Positions: Track job postings, employment trends, and hiring patterns across industries. This data includes job titles, company names, locations, salary ranges, and job descriptions.

Customizable Subsets for Specific Needs Our Employee Dataset is fully customizable, allowing you to filter data based on industry, location, job role, or company size. Whether you need a broad dataset for market analysis or a focused subset for recruitment purposes, we tailor the dataset to your specific needs.

Popular Use Cases

Recruitment & Talent Sourcing: Identify top talent, analyze hiring trends, and enhance recruitment strategies with up-to-date employee data. HR Analytics & Workforce Planning: Optimize workforce management by tracking employee movement, industry hiring patterns, and job market trends. Competitive Intelligence: Monitor hiring activity, employee retention rates, and workforce distribution to gain insights into competitors’ strategies. Market Research & Business Expansion: Analyze employment trends to identify growth opportunities, emerging job markets, and industry shifts. AI & Predictive Analytics: Leverage structured employee data for AI-driven workforce predictions, job market forecasting, and HR automation.

Whether you're looking to improve recruitment, analyze workforce trends, or gain competitive insights, our Employee Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

1) Data Introduction • 'Employee dataset: Employee data' aggregates comprehensive information encompassing various aspects of employee data such as training, surveys, performance, recruitment, and attendance. This rich dataset is designed to support in-depth analysis and research in human resources.

2) Data Utilization (1) Employee dataset: Employee data has characteristics that: • The dataset includes extensive details on employee demographics, job roles, performance ratings, and other HR-related metrics. • It is an invaluable resource for modeling and predicting employee behavior and outcomes based on historical data. (2) Employee dataset: Employee data can be used to: • Human Resources Management: Assists HR professionals in making informed decisions regarding recruitment, training, and employee retention strategies. • Predictive Analysis: Enables companies to forecast trends in employee turnover and performance, aiding in proactive management and planning.

This dataset contains information about employees from different fake companies. It is not real data, and it has been created to resemble real data. The dataset includes various details about the employees, which is useful for research and analysis. It provides a lifelike representation of employee data without compromising privacy or using real personal information.

PredictLeads Job Openings Data provides real-time hiring insights sourced directly from company websites, ensuring the highest level of accuracy and freshness. Unlike job boards that rely on aggregated listings, our dataset delivers unmatched granularity on job postings, salary trends, and workforce demand - making it a powerful tool for HR, talent acquisition, and market analysis.

Use Cases: ✅ Job Boards Enhancement – Improve job listings with, high-quality postings. ✅ HR Consulting – Analyze hiring trends to guide workforce planning strategies. ✅ Employment Analytics – Track job market shifts, salary benchmarks, and demand for skills. ✅ HR Operations – Optimize recruitment pipelines with direct employer-sourced data. ✅ Competitive Intelligence – Monitor hiring activities of competitors for strategic insights.

Key API Attributes:

• id (string, UUID) – Unique job posting identifier.
• title (string) – Job title as posted by the employer.
• description (string) – Full job description.
• url (string, URL) – Direct link to the job posting.
• first_seen_at (ISO 8601 date-time) – When the job was first detected.
• last_seen_at (ISO 8601 date-time) – When the job was last detected.
• contract_types (array of strings) – Employment type (e.g., full-time, contract).
• categories (array of strings) – Job categories (e.g., engineering, sales).
• seniority (string) – Job seniority level (e.g., manager, entry-level).
• salary_data (object) – Salary range, currency, and converted USD values.
• location_data (object) – City, country, and region details.
• tags (array of strings) – Extracted skills and keywords from job descriptions.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/job_openings_dataset

This data collection contains work histories of employees of the Ford Motor Company. A complete work history for each employee is presented, including wage rates, occupation, dates of hire, length of time on the job, reasons for leaving, and job performance ratings. Demographic information in the collection includes date of birth, gender, marital status, race, ethnicity, place of birth, citizenship, and English language ability.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting, employee data / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

Dataset Card for Dataset Name

  Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Supported Tasks and Leaderboards

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  Languages

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  Dataset Structure





  Data Instances

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  Data Fields

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  Data Splits

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  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/nishasharma12/employee-records.

This dataset represents simulated employee information from a modern technology company. It includes demographic details, salary, experience, performance metrics, and work style indicators. Designed for data analysis, visualization, and machine learning projects, this dataset can help explore: 1.Salary trends based on age, experience, and department 2.Impact of remote work on performance 3.Gender and department diversity insights 4.Predictive modelling for performance or salary

💡 Use Cases 1.HR analytics dashboards 2.Predictive performance scoring 3.Salary distribution visualization 4.Machine learning model training & evaluation

HitHorizons Employee Data API gives access to aggregated company data on 80M+ companies from the whole of Europe and beyond.

Company registration data: company name national identifier and its type registered address: street, postal code, city, state / province, country business activity: SIC code, local activity code with classification system year of establishment company type location type

Sales and number of employees data: sales in EUR, USD and local currency (with local currency code) total number of employees sales and number of employees accuracy local number of employees (in case of multiple branches) companies’ sales and number of employees market position compared to other companies in a country / industry / region

Industry data: size of the whole industry size of all companies operating within a particular SIC code benchmarking within a particular country or industry regional benchmarking (EU 27, state / province)

Contact details: company website company email domain (without person’s name)

Invoicing details available for selected countries: company name company address company VAT number

Graph and download economic data for All Employees: Information in Denver-Aurora-Centennial, CO (MSA) (DENV708INFO) from Jan 1990 to Aug 2025 about Denver, information, CO, employment, and USA.

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

➡️ You can select raw or clean and AI-enriched datasets;

➡️ Multiple APIs designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing our APIs.

Coresignal's employee and company data enables you to create and improve innovative data-driven solutions and extract actionable business insights. These datasets are popular among companies from different industries, including investment, sales, and HR technology.

✅ For investors

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal's global Employee Data and Company Data.

Use cases

1. Screen startups and industries showing early signs of growth
2. Identify companies hungry for the next investment
3. Check if a startup is about to reach the next maturity phase

✅ For HR tech

Coresignal's global Employee Data and Company Data enable you to build and improve AI-based talent-sourcing and other HR technology solutions.

Use cases

1. Build AI-based tools
2. Find qualified candidates
3. Enrich existing hiring data

✅ For sales tech

Companies use our large-scale datasets to improve their lead generation engines and power sales technology platforms.

Use cases

1. Extract targeted lead lists
2. Fill in the gaps in your lead data
3. Enable data-driven sales strategies

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

In 2024, Tata Consultancy Services, the multinational information technology service company, had almost ******* employees worldwide. This was a decrease of approximately ****** when compared to the previous year. Tata Consultancy Services Headquartered in Mumbai, India, Tata Consultancy Services provides IT, business consulting, and outsourcing services in over ** countries. For the fiscal year ending March 31, 2023, they had revenue of approximately **** trillion Indian rupees. When broken down by region, more than half of this revenue came from the Americas. By industry vertical, banking, financial services, and insurances made up about ** percent of their total revenue. Business process outsourcing (BPO) market Outsourcing is a business practice when a company hires another company to do other services that could be done internally. As a result, this causes controversy. Those opposed it say that it gets rid of jobs domestically. Those in favor of it say that it helps maintain the nature of the free market because businesses can distribute resources in the most effective manner. In 2019, the global market size of outsourced services amounted to **** billion U.S. dollars. Regionally, most of this revenue came from the Americas. By service type, information technology outsourcing generated **** percent of revenue, while ** percent of the revenue came from business process outsourcing.

Overview: 📃

This dataset provides anonymized sample employee records commonly found in HR information systems. It includes details such as employee ID, name, job title, department, business unit, gender, ethnicity, age, hire date, and annual salary. It is ideal for educational projects, algorithm demonstrations (such as B-tree implementation), HR analytics exploration, salary-related analysis examples, and more.

Columns:

• EEID: Unique Employee Identifier
• Full Name: Sample employee names
• Job Title: Employee's role (e.g., Director, Sr. Manager)
• Department: Department affiliation (e.g., IT, Engineering)
• Business Unit: Business subdivision (e.g., Manufacturing, Specialty Products)
• Gender: Employee gender (Female or Male)
• Ethnicity: Employee ethnicity (Asian, Caucasian, Other)
• Age: Age of the employee
• Hire Date: Date the employee was hired
• Annual Salary: Annual salary in numeric format

This horizontal bar chart displays companies by employee type using the aggregation count. The data is filtered where the sector is Information Technology. The data is about companies.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.