# Naukri Jobs Scraper - Listings, Salary & Skills Data (`webdatalabs/naukri-jobs-pro`) Actor Scrape Naukri.com job listings by keyword and city: title, company, structured salary, skills, experience, work mode and posting date. Optional full job descriptions and AmbitionBox salary benchmarks. Null-honest salary. Export CSV/JSON. No code. - **URL**: https://apify.com/webdatalabs/naukri-jobs-pro.md - **Developed by:** [WebDataLabs](https://apify.com/webdatalabs) (community) - **Categories:** Jobs, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 1,000 job listings This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use 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 Jobs Scraper — Listings, Salary & Skills Data Scrape **Naukri.com** job listings with structured **salary**, **skills**, **experience**, **work mode** and **company** data. Search by keyword and Indian city, filter by experience, work mode and freshness, and export clean JSON/CSV — no code. Optionally pull **full job descriptions** with **AmbitionBox salary benchmarks**, benefits and applicant counts. Built for recruiters, job-market analysts, and HR-tech teams who need reliable Naukri data with **honest salary handling**. Naukri hides the real pay on most postings — so when it's disclosed you get the exact figure (never a fake `0`), and by enabling **Add market salary** you also get an **AmbitionBox market-salary benchmark** (`marketAvgCtcLpa`) for that role and company on every job. ### What data can I extract from Naukri.com? Every job returns a flat, single-record row. High-value fields: | Field | Description | |-------|-------------| | `title`, `companyName` | Role and hiring company | | `salaryMinLpa`, `salaryMaxLpa`, `salaryDisclosed` | Numeric salary in lakhs/annum — `null` + `false` when the employer hid it | | `marketAvgCtcLpa`, `marketMinCtcLpa`, `marketMaxCtcLpa` | AmbitionBox market-pay benchmark for the role + company (enable **Add market salary**) — the salary signal even when Naukri hides the real number | | `experienceMinYears`, `experienceMaxYears`, `seniority` | Experience band + a derived seniority level | | `skills` | Skills/tags list | | `location`, `workMode` | City and office / hybrid / remote | | `companyRating`, `companyReviewCount` | AmbitionBox rating and review count | | `postedDate`, `jobUrl` | Posting date and direct link | Turn on **Add market salary** (under Advanced) for the benchmark on every job — deduped by company+role and cached across runs. With **Fetch full job descriptions** on, each job also gets `descriptionFull`, `keySkillsPreferred`/`keySkillsOther`, `roleCategory`, `industry`, `benefits`, `vacancy` and `applyCount` (and the market benchmark comes bundled). Turn on **AI enrichment** to also add an AI `aiSummary`, a distilled `aiRequirements` list, and a normalized `aiSeniorityNormalized` label per job — no API key of your own required. ### Example output ```json { "jobId": "170426030208", "title": "Senior Data Scientist", "companyName": "Wipro", "companyRating": 3.6, "companyReviewCount": 66203, "salaryDisclosed": true, "salaryMinLpa": 27.5, "salaryMaxLpa": 42.5, "salaryLabel": "27.5-42.5 Lacs", "experienceMinYears": 14, "experienceMaxYears": 18, "experienceLabel": "14-18 Yrs", "seniority": "Senior", "location": "Pune", "workMode": "hybrid", "skills": ["Python", "Azure", "AI/ML", "LLM", "AWS"], "postedDate": "2026-06-23", "jobUrl": "https://www.naukri.com/job-listings-...-170426030208", "sourceKeyword": "data scientist", "sourceLocation": "pune" } ```` When the employer hides the salary (most Naukri postings), the record stays honest — and with **Add market salary** on, it still carries a benchmark: ```json { "title": "Python Developer", "companyName": "Persistent", "salaryDisclosed": false, "salaryMinLpa": null, "salaryMaxLpa": null, "salaryLabel": null, "marketAvgCtcLpa": 22.5, "marketMinCtcLpa": 18, "marketMaxCtcLpa": 30 } ``` ### How to use the Naukri scraper (quick start) 1. Enter one or more **Job keywords** (e.g. `data scientist`, `java developer`). 2. Optionally add **Cities** (e.g. `bangalore`, `mumbai`) — leave empty to search all of India. 3. Set **Max jobs** and, if you like, **Experience**, **Work mode**, or **Posted within**. 4. Run it. Download results as JSON, CSV, or Excel, or pull them via the API. Minimal input: ```json { "keywords": ["data scientist"], "locations": ["bangalore"], "maxJobs": 200 } ``` Recruiter input with full descriptions and market pay: ```json { "keywords": ["backend engineer"], "locations": ["bangalore", "hyderabad"], "experienceYears": "5", "workMode": ["hybrid", "remote"], "fetchFullDescription": true, "maxJobs": 500 } ``` ### How much does it cost to scrape Naukri.com? This actor uses **pay-per-result** — you only pay for the jobs you actually get. See the pricing shown above for the exact rate. Base listings are the cheapest; **full descriptions** and **AI enrichment** are optional and billed separately, so you never pay for extras you don't use. ### Who is this Naukri scraper for? - **Recruiters & staffing** — pull roles by skill + city + experience to map who's hiring and at what pay. - **Job-market & HR analysts** — track hiring volume, salary bands and in-demand skills by role, city and company over time (schedule it with **Only new jobs**). - **Compensation research** — combine disclosed salaries with AmbitionBox market benchmarks to build pay ranges by role and seniority. - **Job boards & aggregators** — ingest fresh Indian job supply with clean, structured fields. - **AI / RAG builders** — feed structured job data (optionally AI-summarized) into search, matching or analytics products. ### Can I filter jobs and monitor new postings? Yes. Filter by **experience**, **work mode** (office/hybrid/remote) and **posting freshness**, or paste full Naukri search-result URLs under Advanced to scrape exactly the filtered searches you build on the site. Turn on **Only new jobs** and schedule the actor to receive just the postings added since your last run — ideal for daily monitoring. ### Frequently asked questions #### Is it legal to scrape Naukri.com? This actor collects **publicly available commercial job postings** (no logins, no personal candidate data). You are responsible for using the data in line with Naukri's terms and applicable laws. It is designed for market research, recruitment and analytics use cases. #### Does it handle hidden salaries? Yes — honestly, and it can still give you a salary signal. Naukri hides the real pay on most postings; when that happens `salaryDisclosed` is `false` and the disclosed figures are `null` (never a fake `0`). Enable **Add market salary** and every job also carries an **AmbitionBox market-salary benchmark** (`marketAvgCtcLpa` plus a typical min/max) for that role at that company — the honest way to get pay data from Naukri. It's deduped by company+role and cached across runs to keep it cheap. #### How many jobs can I scrape per search? A single keyword+city search paginates through Naukri's results; to go wide, add multiple keywords and cities — the actor fans out across every combination and de-duplicates jobs automatically. #### What's the salary format? Salaries are normalized to **LPA** (lakhs per annum) as numeric `salaryMinLpa` / `salaryMaxLpa`, with the original label preserved in `salaryLabel`. #### Do I need a proxy? Yes — Naukri is protected by Akamai. The actor uses Apify residential proxies (India by default) to mint a valid session and rotate on blocks. This is preconfigured; you don't need to set anything up. #### Can I get the full job description? Turn on **Fetch full job descriptions** to add the complete description plus AmbitionBox salary benchmarks, benefits, education and applicant counts to every job. #### Can I get an AI summary of each job? Yes — turn on **AI enrichment** to add a short `aiSummary`, a distilled `aiRequirements` list, and a normalized seniority label to every job. It uses a low-cost model, needs no API key of your own, and is billed at the AI rate. ### Related actors - **AliExpress Product Intelligence** — products, prices, variants and sales signals. - **Reddit Scraper Pro** — posts, comments and sentiment for brand monitoring. - **eBay Scraper Pro** — listings, sold prices and deal signals. Explore more by [WebDataLabs](https://apify.com/webdatalabs). ### Support Found a bug or need a field added? Open an issue on the **Issues** tab and we'll take a look. # Actor input Schema ## `keywords` (type: `array`): Job titles or skills to search, e.g. "data scientist", "java developer". Each keyword is searched across every location below. ## `locations` (type: `array`): Indian cities to search, e.g. "bangalore", "mumbai", "delhi". Leave empty to search all of India for each keyword. ## `maxJobs` (type: `integer`): Total jobs to return across all keyword and city combinations. Keep low for a cheap trial run. ## `experienceYears` (type: `string`): Filter by years of experience (Naukri matches a job's range to a single value — a 3-7 yr job matches 5). ## `workMode` (type: `array`): Filter by work arrangement. Leave empty for any. ## `fetchFullDescription` (type: `boolean`): For each job, also fetch the complete description plus AmbitionBox salary benchmarks, benefits, education and vacancy count. Adds one request per job and is billed at the detailed rate. ## `marketSalary` (type: `boolean`): Adds an AmbitionBox market-salary benchmark (marketAvgCtcLpa) to every job — the real salary signal, since Naukri hides the actual pay on most postings. Deduped by company+role and cached across runs, but slower (browser-based). Off by default. ## `searchUrls` (type: `array`): Optional. Paste full Naukri search-result page URLs (e.g. https://www.naukri.com/data-scientist-jobs-in-bangalore) to scrape exactly those filtered searches. Overrides the keyword/city builder for those URLs. ## `postedWithinDays` (type: `string`): Only return jobs posted within this many days. ## `onlyNewJobs` (type: `boolean`): On scheduled runs, skip jobs already returned in previous runs of the same search. Ideal for monitoring. Uses a named key-value store to remember seen jobs. ## `aiEnrich` (type: `boolean`): Add an AI summary, distilled requirements and a normalized seniority label per job (low-cost OpenAI model). Billed at the AI rate. Off by default. ## `aiApiKey` (type: `string`): Only needed for AI enrichment, and only if you want to use your own key — otherwise the built-in key is used. Your OpenAI API key. ## `useApifyProxy` (type: `boolean`): Required. Naukri is protected by Akamai and blocks bare/datacenter IPs. Residential proxies are used to mint a session token and rotate on blocks. ## `proxyConfiguration` (type: `object`): Defaults to Apify residential proxies in India (recommended for Naukri). Override the group or country here if needed. ## `debug` (type: `boolean`): Verbose logs for token minting, blocks, rotations and enrichment failures. ## Actor input object example ```json { "keywords": [ "data scientist" ], "locations": [ "bangalore", "mumbai" ], "maxJobs": 200, "experienceYears": "any", "workMode": [], "fetchFullDescription": false, "marketSalary": false, "searchUrls": [], "postedWithinDays": "any", "onlyNewJobs": false, "aiEnrich": false, "useApifyProxy": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountryCode": "IN" }, "debug": false } ``` # Actor output Schema ## `overview` (type: `string`): No description ## `salary` (type: `string`): No description ## `full` (type: `string`): No description # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "data scientist" ], "locations": [ "bangalore", "mumbai" ], "maxJobs": 200 }; // Run the Actor and wait for it to finish const run = await client.actor("webdatalabs/naukri-jobs-pro").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "keywords": ["data scientist"], "locations": [ "bangalore", "mumbai", ], "maxJobs": 200, } # Run the Actor and wait for it to finish run = client.actor("webdatalabs/naukri-jobs-pro").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "data scientist" ], "locations": [ "bangalore", "mumbai" ], "maxJobs": 200 }' | apify call webdatalabs/naukri-jobs-pro --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=webdatalabs/naukri-jobs-pro", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Naukri Jobs Scraper - Listings, Salary & Skills Data", "description": "Scrape Naukri.com job listings by keyword and city: title, company, structured salary, skills, experience, work mode and posting date. Optional full job descriptions and AmbitionBox salary benchmarks. Null-honest salary. Export CSV/JSON. No code.", "version": "1.0", "x-build-id": "4oAyMa9i1yjjOV9eT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/webdatalabs~naukri-jobs-pro/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-webdatalabs-naukri-jobs-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/webdatalabs~naukri-jobs-pro/runs": { "post": { "operationId": "runs-sync-webdatalabs-naukri-jobs-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/webdatalabs~naukri-jobs-pro/run-sync": { "post": { "operationId": "run-sync-webdatalabs-naukri-jobs-pro", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "keywords": { "title": "Job keywords", "type": "array", "description": "Job titles or skills to search, e.g. \"data scientist\", \"java developer\". Each keyword is searched across every location below.", "default": [], "items": { "type": "string" } }, "locations": { "title": "Cities", "type": "array", "description": "Indian cities to search, e.g. \"bangalore\", \"mumbai\", \"delhi\". Leave empty to search all of India for each keyword.", "default": [], "items": { "type": "string" } }, "maxJobs": { "title": "Max jobs", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Total jobs to return across all keyword and city combinations. Keep low for a cheap trial run.", "default": 200 }, "experienceYears": { "title": "Experience", "enum": [ "any", "0", "1", "2", "3", "5", "7", "10", "15" ], "type": "string", "description": "Filter by years of experience (Naukri matches a job's range to a single value — a 3-7 yr job matches 5).", "default": "any" }, "workMode": { "title": "Work mode", "type": "array", "description": "Filter by work arrangement. Leave empty for any.", "items": { "type": "string", "enum": [ "office", "hybrid", "remote" ], "enumTitles": [ "Work from office", "Hybrid", "Remote" ] }, "default": [] }, "fetchFullDescription": { "title": "Fetch full job descriptions", "type": "boolean", "description": "For each job, also fetch the complete description plus AmbitionBox salary benchmarks, benefits, education and vacancy count. Adds one request per job and is billed at the detailed rate.", "default": false }, "marketSalary": { "title": "Add market salary benchmark", "type": "boolean", "description": "Adds an AmbitionBox market-salary benchmark (marketAvgCtcLpa) to every job — the real salary signal, since Naukri hides the actual pay on most postings. Deduped by company+role and cached across runs, but slower (browser-based). Off by default.", "default": false }, "searchUrls": { "title": "Naukri search URLs", "type": "array", "description": "Optional. Paste full Naukri search-result page URLs (e.g. https://www.naukri.com/data-scientist-jobs-in-bangalore) to scrape exactly those filtered searches. Overrides the keyword/city builder for those URLs.", "default": [], "items": { "type": "string" } }, "postedWithinDays": { "title": "Posted within", "enum": [ "any", "1", "3", "7", "15", "30" ], "type": "string", "description": "Only return jobs posted within this many days.", "default": "any" }, "onlyNewJobs": { "title": "Only new jobs (incremental)", "type": "boolean", "description": "On scheduled runs, skip jobs already returned in previous runs of the same search. Ideal for monitoring. Uses a named key-value store to remember seen jobs.", "default": false }, "aiEnrich": { "title": "AI enrichment", "type": "boolean", "description": "Add an AI summary, distilled requirements and a normalized seniority label per job (low-cost OpenAI model). Billed at the AI rate. Off by default.", "default": false }, "aiApiKey": { "title": "OpenAI API key (optional)", "type": "string", "description": "Only needed for AI enrichment, and only if you want to use your own key — otherwise the built-in key is used. Your OpenAI API key." }, "useApifyProxy": { "title": "Use Apify proxy", "type": "boolean", "description": "Required. Naukri is protected by Akamai and blocks bare/datacenter IPs. Residential proxies are used to mint a session token and rotate on blocks.", "default": true }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Defaults to Apify residential proxies in India (recommended for Naukri). Override the group or country here if needed.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountryCode": "IN" } }, "debug": { "title": "Debug logging", "type": "boolean", "description": "Verbose logs for token minting, blocks, rotations and enrichment failures.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```