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dustintdn/README.md

Hi, I'm Dustin 👋

Senior Data Scientist @ Aon | Quantitative Methods in the Social Sciences M.A., Columbia University

Welcome to my playground for exploring new project ideas and experiments! I primarily work at the intersection of people analytics, applied ML, and causal inference - using data to answer questions about human behavior at scale. Currently building tools that bring LLMs into real-world workflows during the day, and designing movement-based creative code at night.

Featured Projects 🚀

ProjectDescription
ai-citation-auditCLI tool measuring brand visibility across LLM-powered apps (ChatGPT, Claude, Perplexity)
churn-nbaSmall & medium-sized business (SMB) churn prediction (XGBoost, SHAP) + next-best-action for customer retention
geo-incrementality-experimentCausal geo-experiment measuring incremental lift (CausalImpact)
mmm-budget-optimizationMarketing Mix Model (MMM) to optimize budget allocation for paid media
llm-engagement-summarizerLLM-powered theme extraction from employee engagement surveys
ai-resolution-qualityCausal inference (PSM + DiD) on AI-assisted customer support outcomes
lm-eval-demoBenchmarking SLMs on structured extraction, RAG Q&A, intent classification, and latency
voice-of-customer-analyticsE2E NLP pipeline for customer complaint insights (BERTopic clustering, LLM extraction)

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  1. ai-citation-auditai-citation-auditPublic

    CLI tool for auditing brand visibility across LLMs - queries ChatGPT, Claude, and Perplexity with structured prompt batteries and outputs citation rate, position, and sentiment by competitor

    Python

  2. churn-nbachurn-nbaPublic

    Churn prediction + next-best-action engine for SMB retention - XGBoost model with SHAP explanations, served via FastAPI and Streamlit

    Jupyter Notebook

  3. mmm-budget-optimizationmmm-budget-optimizationPublic

    Marketing Mix Model that quantifies channel contribution and optimizes budget allocation across paid media using PyMC.

    Jupyter Notebook

  4. llm-engagement-summarizerllm-engagement-summarizerPublic

    Analyzing employee engagement survey comments using LLM-powered topic/theme summarization

    Python

  5. geo-incrementality-experimentgeo-incrementality-experimentPublic

    Causal geo-experiment measuring incremental campaign lift with Bayesian structural time series (CausalImpact)

    Jupyter Notebook

  6. lm-eval-demolm-eval-demoPublic

    Benchmarking SLMs on structured extraction, RAG Q&A, intent classification, and latency

    Jupyter Notebook