# Naukri Role Scraper — compare Indian roles by salary (`entrogix_works/naukri-role-benchmark`) Actor

Compare several Naukri searches side by side. One row per role or city: median salary with a confidence range, disclosure rate, experience bar, and how concentrated the postings are among employers. Unofficial; not affiliated with Naukri or Info Edge.

- **URL**: https://apify.com/entrogix\_works/naukri-role-benchmark.md
- **Developed by:** [Entrogix Works](https://apify.com/entrogix_works) (community)
- **Categories:** Jobs
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$30.00 / 1,000 role benchmarkeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Naukri Role Scraper — compare Indian roles by salary

Give **two or more Naukri searches** and get back **one row per role or city**, ranked by median salary — with a confidence range, a disclosure rate, and a number most job-board scrapers never show you: **how much of the "demand" comes from a single poster.**

This is the comparison companion to *Naukri Jobs Scraper*. That one returns one row per job ("what is being advertised"). This one returns one row per search ("which role pays more, and is the demand real").

### What you get per role

| Field | Example |
|---|---|
| `role` | `data analyst in bangalore` |
| `medianSalaryInr` | `1200000` |
| `medianSalaryInrRange` | `[950000, 1450000]` — 95% range for the median |
| `medianSalaryUsd`, `inrPerUsdUsed` | `13636`, `88` — the rate used is always returned |
| `jobs`, `jobsWithSalary`, `salaryDisclosureRate` | `40`, `11`, `0.275` |
| `medianExperienceMinYears` | `3` — the entry bar for this role |
| `uniqueCompanies`, `topPosterShare`, `topPoster` | `18`, `0.35`, `TalentBridge Staffing` |
| `medianCompanyRating`, `medianPostedDaysAgo` | `3.9`, `2` |
| `rank`, `notRankedReason` | `1`, or why the row was left out |

### Why `topPosterShare` is on every row

On Indian job boards a single staffing firm often posts the same requirement dozens of times. Count the listings and you will read that as demand. This Actor reports the share held by the largest poster, and the summary names any role where one company holds **30% or more** — so a large job count can be discounted when it deserves to be.

### What this Actor refuses to do

**Rank on a point estimate alone.** Medians from small samples swing hard. Every row carries a range, and if the top two ranges overlap the summary says `topTwoTooClose: true` — the data cannot tell you which is higher. Roles with fewer than 6 salary-bearing jobs get `rank: null`.

**Hide the denominator.** Naukri listings very often say "Not disclosed". `salaryDisclosureRate` is on every row, because a median drawn from a quarter of the sample is not the same claim as one drawn from all of it.

**Invent an exchange rate.** The USD column uses the fixed rate you pass in, and that rate is returned on every row. Leave it out and no USD figure is produced.

### Summary (key-value store, `BENCHMARK_SUMMARY`)

`roles`, `ranked`, `bestPaying`, `bestPayingMedianInr`, **`topTwoTooClose`**, `medianSalaryDisclosureRate`, **`concentrated`**, `notRanked`.

### Input

```json
{
  "queries": ["data analyst", "data engineer", "data scientist"],
  "samplePerQuery": 40,
  "inrPerUsd": 88
}
```

You can pass plain search terms or Naukri listing URLs. A single query is rejected — one search is not a comparison. You are charged **per role**, not per job.

### Limits, stated plainly

- Salary figures come from what employers chose to publish. They are not payroll data.
- Only job listings are read. Nothing about applicants is collected.

Unofficial; not affiliated with Naukri or Info Edge.

# Actor input Schema

## `queries` (type: `array`):

Two or more Naukri search terms or listing URLs, for example 'data analyst' and 'data engineer', or 'data analyst in bangalore' and 'data analyst in pune'. One search is not a comparison and is rejected.

## `samplePerQuery` (type: `integer`):

Every search is sampled the same way so the comparison stays fair. A larger sample narrows the reported range. You are charged per role, not per job, so raising this does not cost you more.

## `inrPerUsd` (type: `integer`):

Fixed conversion rate used only for the convenience USD figure. The rate you set is returned on every row, so the number is never presented as a live market rate.

## `minIntervalMs` (type: `integer`):

Pacing between fetches. Do not lower this.

## `useProxy` (type: `boolean`):

Not needed in normal use. Turn on only if you see blocks.

## Actor input object example

```json
{
  "queries": [
    "data analyst",
    "data engineer",
    "data scientist"
  ],
  "samplePerQuery": 40,
  "inrPerUsd": 88,
  "minIntervalMs": 3000,
  "useProxy": false
}
```

# Actor output Schema

## `results` (type: `string`):

Every row this run produced, as JSON.

## `summary` (type: `string`):

Which role pays most, whether the top two are separable, and which roles are dominated by a single poster.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "queries": [
        "data analyst",
        "data engineer",
        "data scientist"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("entrogix_works/naukri-role-benchmark").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "queries": [
        "data analyst",
        "data engineer",
        "data scientist",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("entrogix_works/naukri-role-benchmark").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "queries": [
    "data analyst",
    "data engineer",
    "data scientist"
  ]
}' |
apify call entrogix_works/naukri-role-benchmark --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,entrogix_works/naukri-role-benchmark"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/O9G7LbrtDvABYUiJF/builds/0rLUVTCk00pXb8nd9/openapi.json