Gtm Job Market Scan

GTM Job Market Scan

Systematic scan of job boards to convert hiring demand into consulting leads.

Core Thesis

Every senior engineering job posting is a consulting lead. Companies hiring FTEs at $150-250K+ have budget, need, and urgency. A consulting engagement fills the gap faster than a 3-6 month hiring cycle.

Steps

1. Map the Practitioner's Capabilities

Before scanning, read the user's resume/CV and capability docs to extract:

• Exact tool names (OrcaFlex, ANSYS, COMSOL — these become search keywords)
• Domain keywords (subsea, mooring, cathodic protection, FEA)
• Standards knowledge (API 579, DNV, BS 7910 — niche = high value)
• Past clients (these become priority target companies)

Key files in workspace-hub:

• teamresumes/cv/va_resume.md
• docs/BUSINESS_BRAIN.md
• docs/strategy/engineering-chatbot-oilgas-pitch.md
• docs/research/engineering-capability-map.md

2. Create GitHub Issues (Parent + Children)

Structure as a hierarchy:

• Parent: Broad US-wide scan across all verticals
• Child 1: Vertical-specific (e.g., vessel installation contractors)
• Child 2: Vertical-specific (e.g., energy companies)

Labels: cat:strategy, domain:gtm, cat:business Cross-reference issues with comments on each.

Use gh issue create --repo REPO --title "..." --label "..." --body '...' with detailed markdown bodies including:

• Target company lists by tier
• Keyword search matrix
• Capability alignment matrix
• Phased deliverables with checkboxes
• Files to create
• Success criteria

3. Run the Scanner

# Full scan (all 22 keywords + 30 career pages)
/home/vamsee/miniforge3/bin/python scripts/gtm/job-market-scanner.py

# Limited scan (first N keywords, skip career pages)
/home/vamsee/miniforge3/bin/python scripts/gtm/job-market-scanner.py --limit 5 --skip-career-pages

# Custom keywords
/home/vamsee/miniforge3/bin/python scripts/gtm/job-market-scanner.py --keywords "OrcaFlex,mooring,cathodic protection"

4. Review and Prioritize Results

Auto-generated outputs:

• docs/strategy/gtm/job-market-scan/dashboard.md — summary stats
• docs/strategy/gtm/job-market-scan/priority-targets.md — ranked companies
• docs/strategy/gtm/job-market-scan/raw-results/YYYY-MM-DD.json — full data

Focus on "Hot Targets" = companies with 3+ matching open roles.

Pitfalls

Source Reliability (discovered through trial)

• LinkedIn public search: BEST source — returns 60+ results per keyword, no auth needed. Uses linkedin.com/jobs/search/ with sortBy=DD and f_TPR=r604800 (past week).
• Indeed: Blocks with 403 Forbidden ~80% of the time. Intermittently works. Don't rely on it.
• Google Search: Rate-limits aggressively (429 after ~5 requests). Only useful for first 3-4 keywords. Increase REQUEST_DELAY if needed.
• Rigzone: Returned 0 results in testing — site structure may have changed. Selectors need updating.
• Company career pages: Hit-or-miss. Some return useful listings (ABS: 30, Worley: 7), many return 0. Worth running but not primary source.

Keyword Design

• Tier keywords by specificity: "OrcaFlex engineer" (maybe 50 people can do this) scores higher than "FEA analyst" (thousands can do this)
• Include location qualifiers for niche terms: "naval architect Houston"
• Broader terms (Tier 3-4) produce more noise but find manufacturing/aerospace opportunities

Scoring

• Keyword tier: niche (Tier 1) = 80 points, broad (Tier 4) = 20 points
• Senior seniority: +30 points (they need experience NOW)
• Priority company: +25 points
• Houston location: +15 points
• Contract/consulting indicator in title: +20 points

Python Environment

• Use /home/vamsee/miniforge3/bin/python (Python 3.13) — has requests + bs4
• NOT python3 (system Python lacks bs4)
• NOT uv run for this script (it uses --no-project and deps aren't in any pyproject.toml)

Git Workflow

• Check which branch you're on before committing (git branch --show-current)
• If on a feature branch, cherry-pick to main after committing — we hit this exact issue (committed to feat/1668-harness-update-lifecycle by accident, had to cherry-pick to main)
• Push to origin main explicitly
• The weekly-scan-refresh.sh wrapper handles this automatically (checks out main first)

Test Runs Overwrite Raw Results

• Running --limit 2 --skip-career-pages to test overwrites raw-results/YYYY-MM-DD.json with partial data
• The cumulative index is safe (only adds, never removes), but the day's raw file is replaced
• If you need to preserve a full scan, copy the raw file before doing test runs on the same day

Weekly Refresh Setup

The scanner is designed for repeated weekly runs with history tracking.

Cron Infrastructure

• Cron task: gtm-job-market-scan in config/scheduled-tasks/schedule-tasks.yaml
• Schedule: Monday 5AM UTC (0 5 * * 1)
• Wrapper script: scripts/gtm/weekly-scan-refresh.sh — pulls main, runs scanner, commits & pushes
• The wrapper auto-finds Python (miniforge → local → system fallback)

History Tracking (cumulative-index.json)

Each run compares results against cumulative-index.json:

• New jobs: first time seen → highlighted in new-this-week.md
• Returning jobs: seen before → seen_count incremented
• Company history: posting counts per company per scan date → feeds trend report

Auto-Generated Reports (refreshed each scan)

Key Experiential Finding: Persistent Openings

Jobs that appear in 2+ consecutive weekly scans are the hardest to fill. These persistent openings have the HIGHEST consulting conversion rate. The company has budget, has tried hiring, and STILL can't find someone. The trend report surfaces these automatically — prioritize outreach to them.

Output Structure

docs/strategy/gtm/
├── job-market-scan/
│   ├── README.md
│   ├── dashboard.md            (auto-generated)
│   ├── priority-targets.md     (auto-generated)
│   ├── new-this-week.md        (auto-generated, weekly delta)
│   ├── trend-report.md         (auto-generated, week-over-week trends)
│   ├── cumulative-index.json   (all-time job tracking DB)
│   ├── raw-results/
│   │   └── YYYY-MM-DD.json
│   ├── keyword-results/
│   └── company-profiles/
└── vessel-installation-contractors/  (vertical-specific)

scripts/gtm/
├── job-market-scanner.py
└── weekly-scan-refresh.sh      (cron wrapper)

Integration with aceengineer-strategy Repo

The scanner is a DATA SOURCE — results must flow into the aceengineer-strategy private repo which is the GTM command center.

Key aceengineer-strategy files to update after each scan:

• pipeline/prospects.md — add new high-scoring companies
• pipeline/scanner-hot-targets.md — refresh with latest scan rankings
• pipeline/job-scan-integration.md — cross-reference scanner → existing prospects
• strategy/go-to-market.md — update Evolution Log with scan findings
• metrics/weekly-scorecard.md — update "prospects contacted" as outreach happens

Email Templates (created, ready for personalization)

• docs/strategy/gtm/vessel-installation-contractors/email-templates.md — 3-step sequence (Day 0/3/7)
• docs/strategy/gtm/job-market-scan/email-templates-by-vertical.md — 5 vertical variants with {{PLACEHOLDERS}}

Verticals: installation, cathodic protection, FEA/manufacturing, offshore wind, classification societies.

Conference Prep (lives in aceengineer-strategy)

• pipeline/conference-prep-q2-2026.md — master Q2 action plan
• pipeline/otc-2026-meeting-requests.md — 10 companies × 3 message variants

Parallel Execution Pattern

When executing the full GTM cycle, run 4 workstreams in parallel via subagents:

1. Full scan (background terminal — ~10 min)
2. Strategy repo cross-linking (subagent — reads prospects, creates integration docs)
3. Email template drafting (subagent — uses positioning/pricing from strategy repo)
4. Conference prep (subagent — creates action plan with dated checklists)

No git contention: WS-1 writes workspace-hub/docs/strategy/gtm/, WS-2-4 write aceengineer-strategy/pipeline/.

Run P0 expansions in parallel via 3 subagents (one per package). Each subagent gets:

• Existing module listing + init.py exports
• Test file listing
• AGENTS.md conventions (test command, src layout)
• Specific new modules to create with engineering standard references
• Instruction to commit from within digitalmodel/ dir (separate git repo)

Demo Scripts as Sales Collateral

After expanding packages, create self-contained demo scripts at examples/demos/:

• Each demo imports actual library modules and runs real calculations
• Print results in a clean, impressive terminal format
• Include header: "ACE Engineer — [capability]" and footer with contact info
• Test that each runs: PYTHONPATH=src uv run python examples/demos/demo_xxx.py
• Demos become email attachments / GitHub links for prospect outreach

First Scan Baseline (2026-04-02)

• 708 jobs, 460 companies, 22 keywords (full run with history tracking)
• LinkedIn produced ~85% of results (best source by far)
• 121 senior roles, 25 hot targets with 3+ roles each
• Top hits: Oceaneering (11), WSP (12), ABS (9), Jacobs (9), Orsted (6)
• Key discovery: LNG sector was a blind spot (Venture Global, Cheniere both scored high)
• Key discovery: Oceaneering was under-tiered in prospect list (11 roles = Tier 1)

Package Expansion Results (P0 + P1, 2026-04-02)

3 demo scripts created: demo_sn_library.py, demo_pressure_vessel.py, demo_pipeline_cp.py