license: cc-by-4.0
This dataset contains 10,000 synthetic entries representing job postings in supply chain management across industries such as procurement, logistics, and operations. It is designed for benchmarking NLP tasks (e.g., named entity recognition, salary prediction, skill extraction) and analyzing trends in job markets. Fields include job titles, companies, locations, salaries, required skills, and more.
Dataset Structure Each entry includes the following fields:
Job Title (string): Role-specific title.
Most common: "Sourcing Specialist" (6.5%).
Example: "Sourcing Specialist"
Company (string): Hiring organization.
Most common: "Twiga Foods" (6.2%).
Example: "Twiga Foods"
Location (string): Geographic location of the job.
Most common: "Kisumu, Kenya" (10.1%).
Example: "Kisumu, Kenya"
Posting Date (string): Date format DD/MM/YYYY.
Most common: "22/01/2025" (1.4%).
Example: "22/01/2025"
Full Description (string): Detailed job responsibilities.
Example: "Manage supply chain operations, procurement, and logistics..."
Required Skills (string): Semicolon-separated skills.
Most common skill set: "Negotiation; Procurement; Forecasting" (0.4%).
Example: "Negotiation; Procurement; Forecasting"
Experience Level (string): Seniority (e.g., Entry, Senior).
Most common: "Senior" (33.2%).
Salary Range (string): Monthly pay in Kenyan Shillings (KES).
Most common range: "KES 50,000 - 70,000" (20.5%).
Benefits Offered (string): Semicolon-separated benefits.
Most common: "Pension; Transport Allowance" (5%).
Job Category (string): Functional area (e.g., Procurement).
Most common: "Procurement" (17%).
Employment Type (string): Full-Time, Part-Time, etc.
Most common: "Part-Time" (33.7%).
Educational Requirements (string): Minimum qualification.
Most common: "Diploma" (34.7%).
Source URL (string): URL of the job posting (length: 29–100 characters).
Example: "https://example.com/job/20048"
Scrape Timestamp (string): ISO-formatted scrape time.
Example: "2025-03-10T16:14:25Z"
Dataset Creation Source: Synthetically generated to mimic job postings from public boards (e.g., company websites, aggregators).
Time Period: Entries simulate postings dated between 2023–2025.
Preprocessing:
Standardized date formats and salary ranges.
Skills/benefits converted to semicolon-separated strings.
Anonymized company names (if real).
Considerations Geographic Bias: Heavy focus on Kenyan job markets (e.g., salaries in KES, locations like Kisumu).
Temporal Validity: Future dates (e.g., 2025) indicate synthetic data; not suitable for real-time analysis.
Skill/Category Sparsity: Some skills (e.g., "Forecasting") and categories (e.g., "Procurement") appear infrequently.
Licensing License: CC0 1.0 Universal (Public Domain).
Intended Use: Research, NLP model training, labor market analysis.