Disclosure: I built the Apify Actor used in this guide, and it is paid. This article was drafted with an AI assistant. Field names and prices were checked on September 27, 2026.

A company that opens three sales roles this month is about to change how it sells. One that posts its first data engineer is about to buy data tools. Job postings are one of the few buying signals a company publishes on purpose, with dates, in public.

Most signal tools sell that data inside a larger platform. If you already have an account list, you can build the core of it yourself: read each account’s careers page, count what they are hiring for, and flag the changes. This guide does that in Python.

Which hiring signals matter

Pick signals that map to what you sell. Some that hold up in practice:

  • Hiring in your buyer’s team. Selling to RevOps? Watch for sales and RevOps roles. Selling dev tools? Watch engineering.
  • A first hire in a function. “Founding data engineer” or “first marketing hire” means someone is about to pick tools with no incumbent.
  • New leadership. An open VP or director role often comes before a budget and a vendor review.
  • Speed. Many roles posted in the last 30 days, compared with the total open.
  • Expansion. Jobs in a country where the account had none before.
  • Tools in the job text. A post that asks for Salesforce, Snowflake or HubSpot experience tells you what they already run.

None of these proves intent. They tell you where to look first, and they give the first line of an email something true to say.

Step 1: one summary row per account

ATS Jobs API reads the public careers pages of the companies you give it: Greenhouse, Lever, Ashby, Workday and 18 other job board systems. With outputMode set to companies, it returns one summary row per account instead of one row per job.

pip install "apify-client>=3.2,<4"
export APIFY_TOKEN=<YOUR_APIFY_TOKEN>

import os
from decimal import Decimal
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])

with open("accounts.txt") as f:          # one website or domain per line
    accounts = [line.strip() for line in f if line.strip()]

run = client.actor("conserving_celerytop/live-career-page-jobs-api").call(
    run_input={
        "companies": accounts[:500],
        "outputMode": "companies",
        "includeDescription": True,     # adds topTools and first hires found in the job text
    },
    max_total_charge_usd=Decimal("25.00"),  # a hard cap, not the price; see "What it costs"
)

rows = [r for r in client.dataset(run.default_dataset_id).iterate_items() if r["rowType"] == "company"]

Each summary row includes:

Field Meaning
openJobs Open roles
jobsPostedLast7Days, jobsPostedLast30Days Recent roles (null if the board gives no dates)
functionCounts, functionCountsLast30Days Roles per function, such as {"sales": 12, "engineering": 40}
salesShare, engineeringShare Share of sales and engineering roles
leadershipRoles Up to 5 open director, VP and C-level roles, newest first
firstHireRoles Up to 5 roles described as a first or founding hire
topTools Tools named most often in the job text, with a category
countries Where the open roles are

Accounts it cannot resolve come back as rows with rowType set to status and a reason in companyStatus, such as no_job_board_found. Websites work when the site links to its job board; a board link always works best.

Step 2: score the accounts

Here is a simple score for a team that sells to sales leaders. Change the weights to fit your product.

def score(r, function="sales"):
    recent = (r.get("functionCountsLast30Days") or {}).get(function, 0)
    total = (r.get("functionCounts") or {}).get(function, 0)
    leaders = [x for x in (r.get("leadershipRoles") or []) if x.get("jobFunction") == function]
    firsts = [x for x in (r.get("firstHireRoles") or []) if x.get("jobFunction") == function]
    return recent * 3 + total + len(leaders) * 10 + len(firsts) * 15

ranked = sorted(rows, key=score, reverse=True)
for r in ranked[:25]:
    leads = ", ".join(x["title"] for x in (r.get("leadershipRoles") or [])[:2])
    name = r.get("companySlug") or r["company"]
    print(f'{name:<20} score={score(r):>3}  sales_open={(r.get("functionCounts") or {}).get("sales", 0):>3}  {leads}')

Push the top 25 to your CRM with the job titles as context. “Saw you’re hiring a VP Sales and four AEs in Austin” beats a generic opener, and it is true.

Step 3: a weekly list of what changed

A snapshot is useful once. The value is in change. Add three fields and schedule it:

run_input = {
    "companies": accounts[:500],
    "outputMode": "companies",
    "onlyNewJobs": True,
    "monitorName": "accounts-weekly",
}

The first run records every open job. Each later run adds newJobs, closedJobs, newFunctions (functions with open roles now that had none last time, for example ["sales"]) and newCountries. Save the input as a task in Apify Console, add a weekly schedule, and send the result to Slack, Google Sheets or a webhook. An account whose newFunctions contains your buyer’s team is worth a look that week.

What it costs

On September 27, 2026 the Store price was $0.045 per account, with up to 1,000 jobs included, and $0.0428, $0.0405 or $0.036 on paid Apify plans. Prices can change, so check the Store page before a large run. How it adds up:

  • The first run costs one company lookup per account. In companies mode that is the whole price per account, however many jobs it has. So the first run for 500 accounts costs $22.50 on the free plan.
  • Job descriptions, needed for topTools and first hires found in the text, are free on most systems. Workday, Eightfold and a few smaller systems, such as JazzHR and Paylocity, cost $0.01 per started block of 200 jobs described.
  • Each weekly check after the first costs $0.002 per 1,000 open jobs on each board (checked September 27, 2026). Most accounts have fewer than 1,000 open jobs, so 500 accounts cost about $1 a week.
  • max_total_charge_usd in the code is a hard cap. The run stops before it spends more.

Apify’s free plan includes $5 of credit a month.

Limits you should know

  • Function labels are read from titles. Some jobs land in the wrong function, so read the titles before you act on a count. salesShare and engineeringShare use simpler keyword rules.
  • It reads the companies you list. It will not find new accounts that are hiring. Bring a list from your CRM or a data provider.
  • Not every company has a careers page system. Small companies that post only on LinkedIn come back as not found.
  • Old postings are included. Some companies keep evergreen roles open all year. jobsPostedLast30Days separates new hiring from old listings.

FAQ

Is hiring really a buying signal? It is a timing signal. It shows where budget and headcount are going, which is often where new tools are bought. Use it to prioritize, not as proof.

Can I get the hiring manager’s name? No. The Actor reads job postings only and returns no personal data.

How is this different from a signals platform? You get the raw counts and job titles for your own list, at a per-account price, and you decide the scoring. Platforms bundle more sources and contact data.

This article and the Actor are not affiliated with or endorsed by Greenhouse, Lever, Ashby, Workday or any other job board. The Actor reads only jobs published on public careers pages, with no login.