# Ambition Interview Scraper (`getdataforme/ambition-interview-scraper`) Actor Unlock insights into company interview processes with the Ambition Interview Scraper. Effortlessly extract detailed questions, answers, and job profiles from AmbitionBox. Key features: customizable crawling, high-quality JSON output, scalable performance, and real-time timestamps.... - **URL**: https://apify.com/getdataforme/ambition-interview-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Automation, Lead generation, Jobs - **Stats:** 12 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 1,000 results 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/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 ## Ambition Interview Scraper ### Introduction The Ambition Interview Scraper is a powerful Apify Actor designed to extract interview questions and experiences from AmbitionBox, a popular platform for company reviews and career insights. It enables users to gather valuable data on interview processes for specific companies, helping with recruitment strategies, candidate preparation, and market research. By automating the scraping process, this Actor saves time and provides structured, reliable data for informed decision-making. ### Features - **Comprehensive Data Extraction**: Scrapes detailed interview questions, answers, job profiles, and metadata from AmbitionBox. - **Customizable Crawling**: Allows setting limits on requests to control crawl depth and avoid overloading sources. - **High-Quality Output**: Delivers clean, structured JSON data with timestamps and pagination details for easy analysis. - **Reliable and Efficient**: Built on CheerioCrawler for fast, lightweight scraping with robust error handling. - **Flexible Input**: Supports company-specific queries to target relevant interview data. - **Scalable Performance**: Handles multiple pages and large datasets while respecting rate limits. - **Real-Time Insights**: Includes scraped timestamps for tracking data freshness. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | companyName | string | Yes | Name of the company to scrape interview questions for. This should match the company slug on AmbitionBox. | "federal-bank" | | maxRequestsPerCrawl | integer | No | Maximum number of requests that can be made by this crawler. Helps control the scope and prevent excessive API calls. | 100 | ### Example Usage #### Input ```json { "companyName": "federal-bank", "maxRequestsPerCrawl": 100 } ```` #### Output ```json [ { "companyId": 201128, "companyName": "federal-bank", "answerType": "non_coding_gen_ai", "question": "How many branches are there in your area?", "containsHtml": false, "questionUrl": "how-many-branches-are-there-in-your-area-bjk20q37a", "answer": "", "answersCount": 2, "jobProfile": "Associate", "jobProfileUrl": "associate", "topics": [], "createdOn": "", "page": 1, "totalPages": 5, "currentPage": 1, "totalInterviewExperiences": 119, "scrapedAt": "2026-01-23T11:34:35.793Z" } ] ``` ### Use Cases - **Recruitment Analysis**: HR teams can analyze interview questions to refine hiring processes and prepare candidates. - **Candidate Preparation**: Job seekers can review real interview experiences to anticipate questions for specific roles. - **Market Research**: Businesses can benchmark interview practices across companies in their industry. - **Competitive Intelligence**: Gather insights into competitors' hiring strategies and job profiles. - **Academic Research**: Researchers can study trends in interview questions and company cultures. - **Content Creation**: Bloggers and educators can create guides based on aggregated interview data. ### Installation and Usage 1. Search for "Ambition Interview Scraper" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 4. Click "Start" to begin extraction 5. Monitor progress in the log 6. Export results in your preferred format (JSON, CSV, Excel) ### Output Format The Actor outputs an array of JSON objects, each representing an interview experience. Key fields include: - `companyId` and `companyName`: Identifiers for the company. - `question` and `answer`: The interview question and any provided response. - `jobProfile` and `jobProfileUrl`: Details about the role. - `page`, `totalPages`, `currentPage`: Pagination information. - `totalInterviewExperiences`: Total count of experiences scraped. - `scrapedAt`: Timestamp of when the data was extracted. This structured format ensures easy parsing and integration into databases or analytics tools. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! # Actor input Schema ## `companyName` (type: `string`): Name of the company to scrape interview questions for. ## `maxRequestsPerCrawl` (type: `integer`): Maximum number of requests that can be made by this crawler. ## Actor input object example ```json { "companyName": "federal-bank", "maxRequestsPerCrawl": 100 } ``` # 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 = { "companyName": "federal-bank" }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/ambition-interview-scraper").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 = { "companyName": "federal-bank" } # Run the Actor and wait for it to finish run = client.actor("getdataforme/ambition-interview-scraper").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 '{ "companyName": "federal-bank" }' | apify call getdataforme/ambition-interview-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/ambition-interview-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ambition Interview Scraper", "description": "Unlock insights into company interview processes with the Ambition Interview Scraper. Effortlessly extract detailed questions, answers, and job profiles from AmbitionBox. Key features: customizable crawling, high-quality JSON output, scalable performance, and real-time timestamps....", "version": "1.1", "x-build-id": "fewlGjahV7exG080c" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~ambition-interview-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-ambition-interview-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~ambition-interview-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-ambition-interview-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~ambition-interview-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-ambition-interview-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "companyName": { "title": "Company Name", "type": "string", "description": "Name of the company to scrape interview questions for." }, "maxRequestsPerCrawl": { "title": "Max Requests per Crawl", "type": "integer", "description": "Maximum number of requests that can be made by this crawler.", "default": 100 } } }, "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 } } } } } } } } } } ```