Hiring signals for sales

1. 01Filter each board to the signals

   Greenhouse search takes the company's board token and filters by title, department, office, and location on the request. q matches the job title, so one call per signal term keeps only the roles that matter.

   GET /v1/search?engine=greenhouse&board=stripe&page=1 · results.0

   {
     "position": 1,
     "id": "8172487",
     "title": "Abuse Investigator",
     "company": "Stripe",
     "location": "Dublin",
     "remote": false,
     "departments": [
       "8611 Security Analytics"
     ],
     "offices": [
       "Ireland Locations"
     ],
     "url": "https://stripe.com/jobs/search?gh_jid=8172487",
     "first_published": "2026-09-03T17:30:34.000Z",
     "updated_at": "2026-09-12T17:25:25.000Z"
   }

2. 02Diff against yesterday

   Store the job ids you saw. A new id is a new opening; an id that disappears was filled or closed, which is a signal too. first_published tells you when Greenhouse first listed it.

   GET /v1/search?engine=greenhouse&board=stripe&page=1 · results.0.first_published

   "2026-09-03T17:30:34.000Z"

3. 03Read the role before reaching out

   The job endpoint returns the description and, where the company publishes them, pay ranges. The description names the tools the team uses today, which is what your first message should mention.

   GET /v1/job?engine=greenhouse&board=stripe&id=8172487 · job

   {
     "id": "8172487",
     "title": "Abuse Investigator",
     "company": "Stripe",
     "location": "Dublin",
     "departments": [
       "8611 Security Analytics"
     ],
     "offices": [
       "Ireland Locations"
     ],
     "pay_ranges": [
       {
         "title": "Salary",
         "min": 102000,
         "max": 153000,
         "currency": "USD"
       }
     ],
     "url": "https://stripe.com/jobs/search?gh_jid=8172487",
     "paragraphs": [
       "Who we are",
       "About Stripe"
     ]
   }

# Daily diff of open roles on Greenhouse boards: which companies opened
# roles that signal a buying need. Keeps yesterday's job ids in seen.json.
import json, os, pathlib, requests

API = "https://api.clair.im"
HEADERS = {"Authorization": f"Bearer {os.environ['CLAIR_API_KEY']}"}
BOARDS = ["stripe", "airbnb", "figma"]
SIGNALS = ["data engineer", "revenue operations", "security engineer"]
STATE = pathlib.Path("seen.json")
seen = json.loads(STATE.read_text()) if STATE.exists() else {}

def jobs(board, q):
    r = requests.get(f"{API}/v1/search", headers=HEADERS, timeout=60,
                     params={"engine": "greenhouse", "board": board, "q": q})
    r.raise_for_status()
    return r.json()["results"]

for board in BOARDS:
    before = set(seen.get(board, []))
    now = {}
    for q in SIGNALS:  # q matches the job title
        for job in jobs(board, q):
            now[job["id"]] = job
    for job_id in now.keys() - before:
        job = now[job_id]
        print(f"{job['company']}: {job['title']} ({job['location']}) {job['url']}")
    seen[board] = sorted(now)

STATE.write_text(json.dumps(seen, indent=2))

Set CLAIR_API_KEY to a key subscribed to the Greenhouse Jobs API. Each call is one request against that API's monthly quota.

• Only companies that use Greenhouse and publish a public board. Find the board token in the company's careers URL.
• Greenhouse's own Job Board API is public and free. If you only need Greenhouse and can write the filtering, call it directly; Clair saves the filtering and gives the same schema as its Indeed and Glassdoor APIs.
• Search returns 25 jobs per page. A large company's unfiltered board can run to dozens of pages, so filter on the request.
• Titles are free text. A signal term such as data engineer misses Analytics Engineer; list the variants.