Interview Feedback → Hiring Persona

import os, requests, time, base64
from collections import defaultdict

GH_KEY = os.environ["GREENHOUSE_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
GH_BASE = "https://harvest.greenhouse.io/v1"

gh_auth = base64.b64encode(f"{GH_KEY}:".encode()).decode()
GH_H = {"Authorization": f"Basic {gh_auth}"}

# 1. Pull recent applications with outcomes
applications = []
page = 1
while len(applications) < 300:
    batch = requests.get(f"{GH_BASE}/applications",
        headers=GH_H,
        params={"per_page": 100, "page": page, "status": "hired"}).json()
    if not batch:
        break
    applications.extend(batch)
    page += 1
    time.sleep(0.3)

rejected_apps = []
page = 1
while len(rejected_apps) < 300:
    batch = requests.get(f"{GH_BASE}/applications",
        headers=GH_H,
        params={"per_page": 100, "page": page, "status": "rejected"}).json()
    if not batch:
        break
    rejected_apps.extend(batch)
    page += 1
    time.sleep(0.3)

hired_app_ids = {a["id"] for a in applications}
rejected_app_ids = {a["id"] for a in rejected_apps}

# 2. Pull scorecards for these applications
hired_scores = defaultdict(list)
rejected_scores = defaultdict(list)

for app_id in list(hired_app_ids)[:50]:
    scorecards = requests.get(f"{GH_BASE}/applications/{app_id}/scorecards",
        headers=GH_H).json()
    for sc in scorecards:
        for attr in sc.get("attributes", []):
            name = attr.get("name", "Unknown")
            rating = attr.get("rating", "")
            if rating:
                hired_scores[name].append(rating)
    time.sleep(0.25)

for app_id in list(rejected_app_ids)[:50]:
    scorecards = requests.get(f"{GH_BASE}/applications/{app_id}/scorecards",
        headers=GH_H).json()
    for sc in scorecards:
        for attr in sc.get("attributes", []):
            name = attr.get("name", "Unknown")
            rating = attr.get("rating", "")
            if rating:
                rejected_scores[name].append(rating)
    time.sleep(0.25)

# 3. Build analysis context
RATING_MAP = {"definitely_not": 1, "no": 2, "mixed": 3, "yes": 4, "strong_yes": 5}

analysis_lines = []
all_attrs = set(hired_scores.keys()) | set(rejected_scores.keys())
for attr in sorted(all_attrs):
    h_vals = [RATING_MAP.get(r, 3) for r in hired_scores.get(attr, [])]
    r_vals = [RATING_MAP.get(r, 3) for r in rejected_scores.get(attr, [])]
    h_avg = sum(h_vals) / len(h_vals) if h_vals else 0
    r_avg = sum(r_vals) / len(r_vals) if r_vals else 0
    delta = h_avg - r_avg
    analysis_lines.append(f"- {attr}: Hired avg={h_avg:.1f} Rejected avg={r_avg:.1f} Delta={delta:+.1f}")

analysis_block = "\n".join(analysis_lines)

# 4. Mave synthesis
mave = requests.post("https://app.mavera.io/api/v1/mave/chat",
    headers={"Authorization": f"Bearer {MV}", "Content-Type": "application/json"},
    json={"message": f"""Analyze these interview scorecard attributes comparing hired vs rejected candidates.

SCORECARD DATA ({len(hired_app_ids)} hired, {len(rejected_app_ids)} rejected applications analyzed):

{analysis_block}

Produce:
1. Top 5 traits that predict successful hires (highest delta)
2. Traits that DON'T predict success (low delta — possible interviewer bias)
3. Recommended interview rubric adjustments
4. Calibration notes for interviewers
5. A "success persona" profile summarizing the ideal candidate"""}).json()

print("=== Hiring Success Persona ===")
print(mave.get("content", "")[:2000])

const GH_KEY = process.env.GREENHOUSE_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const GH_BASE = "https://harvest.greenhouse.io/v1";
const GH_H = { Authorization: `Basic ${btoa(`${GH_KEY}:`)}` };

const RATING_MAP = { definitely_not: 1, no: 2, mixed: 3, yes: 4, strong_yes: 5 };

async function ghGet(path, params = {}) {
  const qs = new URLSearchParams(params).toString();
  const res = await fetch(`${GH_BASE}${path}?${qs}`, { headers: GH_H });
  if (res.status === 429) { await new Promise((r) => setTimeout(r, 10000)); return ghGet(path, params); }
  if (!res.ok) throw new Error(`GH ${res.status}`);
  return res.json();
}

async function fetchApps(status, max = 300) {
  const apps = [];
  let page = 1;
  while (apps.length < max) {
    const batch = await ghGet("/applications", { per_page: 100, page, status });
    if (!batch.length) break;
    apps.push(...batch);
    page++;
    await new Promise((r) => setTimeout(r, 300));
  }
  return apps;
}

// 1. Applications
const hiredApps = await fetchApps("hired");
const rejectedApps = await fetchApps("rejected");

// 2. Scorecards
async function collectScores(appIds) {
  const scores = {};
  for (const id of appIds.slice(0, 50)) {
    const scorecards = await ghGet(`/applications/${id}/scorecards`);
    for (const sc of scorecards) {
      for (const attr of sc.attributes || []) {
        const name = attr.name || "Unknown";
        if (attr.rating) (scores[name] ??= []).push(attr.rating);
      }
    }
    await new Promise((r) => setTimeout(r, 250));
  }
  return scores;
}

const hiredScores = await collectScores(hiredApps.map((a) => a.id));
const rejectedScores = await collectScores(rejectedApps.map((a) => a.id));

// 3. Analysis
const allAttrs = new Set([...Object.keys(hiredScores), ...Object.keys(rejectedScores)]);
const lines = [...allAttrs].sort().map((attr) => {
  const hVals = (hiredScores[attr] || []).map((r) => RATING_MAP[r] || 3);
  const rVals = (rejectedScores[attr] || []).map((r) => RATING_MAP[r] || 3);
  const hAvg = hVals.length ? hVals.reduce((s, v) => s + v, 0) / hVals.length : 0;
  const rAvg = rVals.length ? rVals.reduce((s, v) => s + v, 0) / rVals.length : 0;
  return `- ${attr}: Hired avg=${hAvg.toFixed(1)} Rejected avg=${rAvg.toFixed(1)} Delta=${(hAvg - rAvg) > 0 ? "+" : ""}${(hAvg - rAvg).toFixed(1)}`;
});

// 4. Mave
const mave = await fetch("https://app.mavera.io/api/v1/mave/chat", {
  method: "POST",
  headers: { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" },
  body: JSON.stringify({
    message: `Analyze scorecard attributes (${hiredApps.length} hired, ${rejectedApps.length} rejected):\n\n${lines.join("\n")}\n\nProduce: 1) Top 5 predictive traits 2) Non-predictive traits 3) Rubric adjustments 4) Calibration notes 5) Success persona`,
  }),
}).then((r) => r.json());

console.log("=== Hiring Success Persona ===");
console.log((mave.content || "").slice(0, 2000));