fix(F14): call keyword relevance what it is, and teach it German

The scout presented a keyword count as "Strong fit" / "Medium fit". It is
neither: it measures how many profile keywords a posting echoes, and knows
nothing about his evidence, the hard requirements, the level, the language or
the commute.

- Buckets renamed to keyword relevance, with "NOT a fit judgement" in the
  heading, and score_job documents the limit with the review's own example:
  "Senior AI Engineer, Generative AI / LLM / RAG" scores 12 against "Senior Data
  Engineer" at 3, while the second is much closer to what he can evidence.
- German job vocabulary added. Every German-language posting used to score near
  zero regardless of fit, which is the real reason RUAG, SBB, BKW, PostFinance
  and Kdo Cy all needed a _score_floor. The floor was a workaround; the missing
  terms were the defect. "Senior Dateningenieur" 1 -> 3, level with its English
  twin.
- Platform/DevOps vocabulary was simply absent: devsecops, platform engineer,
  platform automation, infrastructure as code, observability, site reliability.
  The Kdo Cy "ICT-System Engineer (DevSecOps / Platform Automation)" scored ZERO
  before this - a posting whose requirement lines match his canonical evidence
  almost line for line. It now scores 10.
- Noise is unaffected: "Office Manager:in Administration" still scores 0.

Floors were left in place so this diff stays attributable; they can likely be
reduced now that the vocabulary exists.

34/34 tests pass; validated on a live 37-company run.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Qej5SBvhcbGXHwLqNWiskM

job_scout/scout.py

@@ -93,6 +93,23 @@ GLOBAL_SCOPE_KEYWORDS = [
 ]
 
 POSITIVE_KEYWORDS = {
+    # German/Swiss job titles (added 2026-09-11, F14). Without these, every German-language
+    # posting scored near zero regardless of fit, which is why RUAG, SBB, BKW, PostFinance
+    # and Kdo Cy all needed a _score_floor to stay visible at all. The floor is a workaround;
+    # these terms are the actual fix. Weights mirror their English counterparts.
+    # Platform/DevOps vocabulary that was simply absent (F14). The Kdo Cy
+    # "ICT-System Engineer (DevSecOps / Platform Automation)" scored ZERO before this -
+    # a posting whose every requirement line matches his canonical evidence.
+    "devsecops": 3, "platform engineer": 3, "platform automation": 3, "platform": 1,
+    "automation": 1, "infrastructure as code": 2, "observability": 2,
+    "site reliability": 3, "system engineer": 1, "ict": 1,
+    "dateningenieur": 2, "data engineer (m/w/d)": 2, "systemingenieur": 2,
+    "software-entwickler": 2, "softwareentwickler": 2, "entwickler": 1,
+    "plattform": 2, "plattform-engineer": 3, "datenplattform": 3,
+    "automatisierung": 2, "betrieb": 1, "datenanalyse": 2, "datenmodellierung": 2,
+    "schnittstellen": 1, "datenqualitaet": 2, "datenqualität": 2,
+    "künstliche intelligenz": 3, "kuenstliche intelligenz": 3, "maschinelles lernen": 3,
+    "cloud-plattform": 2, "containerisierung": 2, "informatiker": 1,
     "genai": 3, "generative ai": 3, "llm": 3, "large language model": 3,
     "applied ai": 3, "applied ml": 3, "ai engineer": 3, "ml engineer": 3,
     "mlops": 3, "ai platform": 3, "ml platform": 3,
@@ -1819,6 +1836,15 @@ def _kw_in(kw, text):
 
 
 def score_job(job, title_only=False):
+    """Keyword RELEVANCE for search ranking - explicitly not a fit score (F14).
+
+    It measures how many profile keywords a posting echoes. It does not know his evidence,
+    the hard requirements, the level, the language or the commute, and a higher number must
+    never be read as a better application. Compare: "Senior AI Engineer, Generative AI / LLM
+    / RAG" scores 12 against "Senior Data Engineer" at 3, while the second is much closer to
+    what he can actually evidence. German titles score near zero regardless of fit, which is
+    why pre-filtered boards carry a _score_floor.
+    """
     # Title carries the real signal; the JD body is full of company boilerplate (every
     # Kraken post mentions crypto/blockchain/trading, every cloud post mentions python).
     # So title matches score at full weight and body-only matches at half (min 1) — enough
@@ -2130,10 +2156,14 @@ def write_report(path, results, errors, new_only, include_weak, stats=None, tota
         lines.append(f"_{n_decided} role(s) already in the decision log ({shown}; "
                      f"see state/decisions.json)._")
 
-    buckets = [("Strong fit (score >= 6)", strong),
-               ("Medium fit (score 2-5)", medium)]
+    # F14: this number is SEARCH RELEVANCE - how many profile keywords the posting echoes.
+    # It is not a fit assessment and must never be presented as one: "Senior AI Engineer"
+    # with GenAI/LLM/RAG scores 12 while "Senior Data Engineer" scores 3, although the
+    # second sits far closer to his actual evidence. Fit is decided on the full JD.
+    buckets = [("High keyword relevance (score >= 6) - NOT a fit judgement", strong),
+               ("Medium keyword relevance (score 2-5)", medium)]
     if include_weak:
-        buckets.append(("Weak / noise (score < 2)", weak))
+        buckets.append(("Low keyword relevance (score < 2) - German titles land here", weak))
 
     for bucket_name, bucket in buckets:
         shown = [r for r in bucket