{
  "name": "Samuel Martin",
  "location": "San Francisco Bay Area",
  "yearsExperience": "8+",
  "currentRole": {
    "title": "Lead, Product Analytics & Experimentation",
    "employer": "Apree Health",
    "since": "2024-05"
  },
  "contact": {
    "email": "hello@samuelsmartin.xyz",
    "linkedin": "https://linkedin.com/in/samuel-senkichi-martin",
    "github": "https://github.com/Senkichi"
  },
  "proofPoints": [
    {
      "value": "3.45x",
      "claim": "lift in appointment booking conversion, outbound randomized controlled trial",
      "headline": true,
      "evidence": "/work/outbound-rct"
    },
    {
      "value": "$99",
      "claim": "cost per acquisition in the treatment arm",
      "headline": true,
      "evidence": "/work/outbound-rct"
    },
    {
      "value": "$549",
      "claim": "first-year customer value, against which the $99 acquisition cost was priced",
      "headline": true,
      "evidence": "/work/outbound-rct"
    },
    {
      "value": "$1.1M",
      "claim": "scaling roadmap funded after the trial, sensitivity tested before executive review",
      "headline": true,
      "evidence": "/work/outbound-rct"
    },
    {
      "value": "750+",
      "claim": "enterprise clients covered by the analytics function Samuel Martin led",
      "headline": true,
      "evidence": "/work/"
    },
    {
      "value": "5.5M",
      "claim": "users behind those enterprise clients",
      "headline": true,
      "evidence": "/work/"
    },
    {
      "value": "Weeks to minutes",
      "claim": "financial reporting latency, after the unified attribution warehouse",
      "headline": false,
      "evidence": "/work/attribution-warehouse"
    }
  ],
  "skills": [
    {
      "group": "Experimentation & Causal Inference",
      "skills": [
        "A/B Testing & Experimentation Design",
        "Randomized Controlled Trial (RCT) Design",
        "Difference-in-Differences",
        "Hierarchical Linear Modeling (Mixed Effects)",
        "Intent-to-Treat Analysis",
        "Causal Inference Strategy & Experimental Design"
      ]
    },
    {
      "group": "Growth & Marketing Analytics",
      "skills": [
        "Marketing Attribution & Multi-Channel Measurement",
        "Unit Economics (LTV, CAC, ROI)",
        "Conversion Funnel Optimization",
        "KPI Definition & Metric Frameworks",
        "E-commerce & Marketplace Analytics"
      ]
    },
    {
      "group": "Data Platforms & Tooling",
      "skills": [
        "SQL (BigQuery, PostgreSQL, MySQL)",
        "Python (Pandas, SciPy, Statsmodels, Scikit-learn)",
        "R",
        "dbt",
        "Salesforce (Marketing Cloud, Service Cloud)",
        "Google Cloud Platform",
        "Tableau"
      ]
    }
  ],
  "archetypes": [
    {
      "id": "growth",
      "url": "/for/growth",
      "audience": "For growth and marketing analytics",
      "headline": "Outbound Stopped Being a Cost Line",
      "owns": "The experiment design, the attribution model underneath it, and the reporting that a finance partner will accept without a translation layer."
    },
    {
      "id": "strategy",
      "url": "/for/strategy",
      "audience": "For strategy and business analytics",
      "headline": "A $1.1M Roadmap That Survived the Executive Room",
      "owns": "Framing the decision, choosing the horizon it should be judged on, and carrying the analysis into the room where the money is allocated."
    },
    {
      "id": "product",
      "url": "/for/product",
      "audience": "For product data science",
      "headline": "Measuring a Feature Without Fooling Yourself",
      "owns": "The experiment and quasi-experiment design, the event taxonomy underneath it, and the reporting discipline that keeps a leading indicator from being quoted as an outcome six months later."
    },
    {
      "id": "healthcare-ai",
      "url": "/for/healthcare-ai",
      "audience": "For healthcare and applied AI",
      "headline": "Marketing Data Joined to Clinical Records, Countably",
      "owns": "The measurement architecture across protected data, the joins that make it auditable, and the honest evaluation of AI features that arrive with their own novelty premium."
    }
  ],
  "confidentiality": "Most of this work sits behind protected health information. Figures on this site are redrawn on synthetic data, no client is named, and the method and reasoning are exact.",
  "updated": "2026-07-24"
}