AI Systems Architect & Marketing Science Engineer

I build production-grade AI systems that generate measurable revenue and withstand

graph LR
  A[Event Stream<br/>Kafka] --> B[Feature Store<br/>Delta Lake]
  B --> C[Attribution Engine<br/>Markov + Shapley]
  C --> D[Bayesian UQ<br/>Confidence Bounds]
  D --> E[API Layer<br/><100ms p99]
  E --> F[Client Dashboard<br/>Real-time Insights]

  style A fill:#f9f,stroke:#333,stroke-width:2px
  style C fill:#bbf,stroke:#333,stroke-width:2px
  style D fill:#bfb,stroke:#333,stroke-width:2px
  style E fill:#fbb,stroke:#333,stroke-width:2px

Most "attribution" is just weighted correlation with extra steps. I build systems grounded in first-principles causal frameworks:

• Markov chain state modeling for temporal causality (not just last-touch heuristics)
• • Shapley value decomposition for fair marginal contribution (game-theoretic fairness)
  • • Bayesian uncertainty quantification to bound epistemic vs. aleatoric error
    • • Real-time probabilistic identity resolution for streaming platforms (Kafka + Ray)
      • Why this matters: Resolves the fundamental gap between "correlation that shipped" and "causation that scales."

      • Production ML Infrastructure

      • End-to-end data engineering for AI systems that don't explode in production:
      • • Event streaming pipelines: Apache Kafka, Delta Lake, CDC (change data capture)
        • • Distributed compute: Ray, Dask, orchestration with Airflow/Prefect
          • • Feature stores & versioning: MLflow, DVC for reproducible experiments
            • • Observability: Prometheus, Grafana, custom drift detection (Kolmogorov-Smirnov tests)
              • Recent case: Live event attribution engine for WWE Raw on Netflix—second-screen correlation with <2s latency during live broadcasts.

              • Marketing Science & Growth Systems

              • Behavioral profiling, audience segmentation, and revenue optimization:
              • • Psychographic priors for context-aware attribution (not just demographics)
                • • Treatment effect heterogeneity to identify high-value segments (CATE estimation)
                  • Multi-armed bandit optimization for dynamic creative allocation
                  • LLM-augmented research: Automated product discovery (2.6 sale-ready products/day, zero manual work)

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                  • 🏆 Case Studies

                  • | System | Problem | Solution | Outcome |
                  • |--------|---------|----------|---------|
                  • | Geospatial Lead Gen Engine | Insurance carrier needed qualified leads in underserved zip codes | ML classification on demographic + property data; automated outreach sequencing | 214,384 qualified leads at 99.6% accuracy |
                  • | Contact Rate Optimizer | SaaS company had 30% connect rate, burning sales budget | Attribution-informed timing + messaging personalization via behavioral clustering | 30% → 70% contact rate improvement |
                  • | Product Research Automation | E-commerce brand spent 8 hrs/day on manual product research | LLM-powered competitive analysis + trend detection; automated scoring | 2.6 products/day flagged as sale-ready, 100% automation |
                  • | Streaming Identity Resolution | Ad platform needed real-time user matching across devices (GDPR-compliant) | Probabilistic graph matching with Bayesian priors; <100ms p99 latency | 78% accuracy at scale, fully GDPR/CCPA compliant |

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                  • 🛠️ Core Stack

                  • Languages & Frameworks
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                  • Data & ML Infrastructure
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                  • Specialized
                  • Bayesian Statistics · Causal Inference (DoWhy, EconML) · LLMs (Claude, GPT-4) · Make.com · Shapley Values · Markov Chains

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                  • 📂 Selected Projects

                  • 👇 Pinned repositories below showcase production-grade systems:
                  • first-principles-attribution: Causal framework resolving correlation vs. causation with Markov/Shapley/Bayesian UQ
                  • probabilistic-identity-resolution: Real-time streaming identity graph for multi-device attribution
                  • behavioral-profiling-attribution: Context-aware attribution with psychographic priors (30% ROI lift)
                  • live-event-attribution-wwe-raw: Second-screen correlation engine for sports advertising
                  • portfolio-hub: Next.js command center showcasing 10+ production attribution systems

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                  • 🔨 Currently Building

                  • Multi-touch attribution whitepaper (v2.0): Formalizing the epistemic gap in correlation-based attribution models
                  • Streaming feature store: Real-time feature computation for sub-100ms inference pipelines
                  • Open-source attribution library: First-principles toolkit for marketing science teams

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                  • 📫 Let's Connect

                  • Open to:
                  • ✅ Consulting engagements (attribution systems, ML infrastructure, data science strategy)
                  • ✅ Speaking & workshops (marketing science, causal inference, production ML)
                  • ✅ Advising high-growth startups on data/AI architecture
                  • Reach me:
                  • 🔗 LinkedIn · Portfolio · Email
                  • 📅 Book a 30-min intro call (if interested in consulting)

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                  • _{💡 Pro tip: If you're building attribution systems, check out my first-principles framework — it's the only open-source implementation of Markov+Shapley+Bayesian UQ I've seen that doesn't collapse into weighted last-touch under pressure.}

• 🎓 Make Foundation Certified — Advanced automation & integration specialist
• • 📚 Open-source contributor — First-principles attribution framework (Markov+Shapley+Bayesian UQ)

  • • 🏢 Fortune 1000 experience — Built secure systems for $5.4B market cap finance department

    • • 🚀 0→1 builder — Scaled online community from 0 to 1,200 active members in 4 months

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      💬 "The only way to do great work is to love what you do." — Steve Jobs

      Let's build something remarkable together.