---
id: "employee-salaries"
slug: "employee-salaries"
name: "Employee Salaries"
domain: "Human Resources & Workforce Analytics"
taskType: "regression"
numRows: 9228
numColumns: 11
targetColumn: "current_annual_salary_usd"
primaryMetric: "RMSE"
primaryScore: "8,126"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-tabular-benchmarks/resolve/main/data/employee-salaries/employee-salaries.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/12x8w1hTNNGj6Y3Zscxeitqnn7H5e3ltBZHp5A8UbOxM/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/employee-salaries"
---

# Dataset: Employee Salaries (employee-salaries)

> The Employee Salaries dataset provides compensation records for 9,228 active, permanent public sector employees in Montgomery County, MD. The primary predictive objective is tabular regression to estimate current annual salary based on departmental placement, job titles, tenure, and historical earnings.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `regression` |
| **Business Domain** | Human Resources & Workforce Analytics |
| **Target Column** | `current_annual_salary_usd` |
| **Rows (Instances)** | 9,228 |
| **Features (Columns)** | 11 |
| **Primary Evaluation Metric** | RMSE |
| **Best Benchmark Score** | **8,126** |

## Business Decision Context
**Context:** Public sector human resources departments and enterprise compensation teams manage complex salary structures across diverse agencies, divisions, and union-negotiated pay bands. Ensuring fair compensation, standardizing career progressions, and budgeting across municipal departments requires accurate salary modeling.
**Objective:** Predict the base current annual salary for municipal employees using demographic, positional, departmental, and historical tenure attributes.
**Decision Impact:** Improves payroll forecasting accuracy, eliminates pay parity anomalies across agencies, and optimizes municipal budget allocations. Overestimating salaries leads to inflated budget reserves and fiscal waste, while underestimating leads to retention risks, labor disputes, and compensation non-compliance.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `current_annual_salary_usd` | `numeric` | target | 3,403 | 0 | Target variable representing the standardized base annual salary contracted for the employee in USD. |
| `full_name` | `string` | feature | 9,222 | 0 | Full legal name of the county employee in 'Last, First Middle' format. |
| `gender` | `categorical` | feature | 2 | 17 | Recorded biological sex or administrative gender of the employee ('F' for Female, 'M' for Male). |
| `gross_pay_received_2016_usd` | `numeric` | leakage | 8,977 | 100 | Total gross compensation paid to the employee during calendar year 2016, including base salary, overtime, and bonuses. |
| `overtime_pay_2016_usd` | `numeric` | leakage | 6,176 | 2917 | Cumulative compensation paid specifically for overtime hours worked during calendar year 2016. |
| `department_code` | `categorical` | feature | 37 | 0 | Standardized municipal department acronym or abbreviation (e.g., 'POL' for Police, 'HHS' for Health & Human Services, 'COR' for Correction & Rehabilitation). |
| `department_name` | `categorical` | feature | 37 | 0 | Full formal name of the municipal operating agency or government department. |
| `division_name` | `string` | feature | 694 | 0 | Specific internal operational division, bureau, or administrative unit within the department. |
| `assignment_category` | `categorical` | feature | 2 | 0 | Employment status classification indicating full-time vs. part-time and regular vs. temporary status (e.g., 'Fulltime-Regular'). |
| `position_title` | `string` | feature | 385 | 0 | Formal job title and grade level held by the employee (e.g., 'Master Police Officer', 'Social Worker IV'). |
| `underfilled_job_title` | `string` | feature | 84 | 8135 | Lower classification title assigned when an employee is performing duties of a higher grade position while in training or trainee status. |
| `date_first_hired` | `string` | feature | 2,264 | 0 | Calendar date on which the employee was first hired into county service in MM/DD/YYYY format. |
| `year_first_hired` | `numeric` | feature | 51 | 0 | Four-digit calendar year corresponding to the employee's initial appointment date, proxying organizational seniority. |

## Empirical Benchmark Leaderboard
| Rank | Algorithm / Model | Model Family | Runtime | R² Score | MAE | RMSE |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **EXAONE Tabular** | Tabular Foundation Model | PyTorch/Python | **0.9348** | 3,872.1 | 7,426.9 |
| 2 | **Streaming nanotabicl** | Tabular Foundation Model | PyTorch/Python | **0.9267** | 4,148.7 | 7,874.3 |
| 3 | **nanotabicl Vanilla** | Tabular Foundation Model | PyTorch/Python | **0.9221** | 4,246.2 | 8,108.9 |
| 4 | **Carla Engine (TabICLv2 WebGPU)** | Tabular Foundation Model | Browser | **0.9221** | 4,293.3 | 8,121.2 |
| 5 | **Full TabICLv2 (PyTorch Reference)** | Tabular Foundation Model | PyTorch/Python | **0.9218** | 4,253.5 | 8,125.8 |
| 6 | **TabPFN v3** | Tabular Foundation Model | PyTorch/Python | **0.9184** | 4,260.1 | 8,308 |
| 7 | **Google TabFM v1.0** | Tabular Foundation Model | PyTorch/Python | **0.8713** | 4,882.7 | 10,434.5 |

### Repeated Cross-Validation Fold Scores (TabICLv2 Reference)
| Fold | Train Samples | Test Samples | Metric Score | Latency |
| :--- | :--- | :--- | :--- | :--- |
| Fold 0 | 6152 | 3076 | 7926.7340 | 2116ms |
| Fold 1 | 6152 | 3076 | 8287.1869 | 2087ms |
| Fold 2 | 6152 | 3076 | 9056.0109 | 2188ms |
| Fold 0 | 6152 | 3076 | 7806.8229 | 2073ms |
| Fold 1 | 6152 | 3076 | 8026.4760 | 2191ms |
| Fold 2 | 6152 | 3076 | 8258.7975 | 2082ms |
| Fold 0 | 6152 | 3076 | 8143.4268 | 2177ms |
| Fold 1 | 6152 | 3076 | 8070.0988 | 2104ms |
| Fold 2 | 6152 | 3076 | 7556.4174 | 2192ms |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-tabular-benchmarks/resolve/main/data/employee-salaries/employee-salaries.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/12x8w1hTNNGj6Y3Zscxeitqnn7H5e3ltBZHp5A8UbOxM/edit?usp=sharing)
- **OpenML Source:** [OpenML Dataset](https://www.openml.org/d/42125)
- **Academic Paper / Citation:** [Read Research Paper](https://data.montgomerycountymd.gov/api/views/544h-qxyx/rows.csv)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/employee-salaries](https://carlahq.eu/datasets/employee-salaries)