Advanced Statistical Consulting

When Standard Analytics Aren't Enough

Some problems can't be solved with a BI tool, a regression in a spreadsheet, or an off-the-shelf ML library. They require deep statistical thinking — the kind that accounts for censored data, small samples, causal structure, and decision-theoretic framing.

I bring graduate-level statistical expertise to practical business problems. Whether you need a survival model for customer lifetime value, a Bayesian model for a problem with limited data, or a causal inference framework to evaluate a policy change — I can help.

Areas of Expertise

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  Bayesian Modeling: Small-sample inference, hierarchical models, and decision analysis with principled uncertainty quantification.
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  Survival Analysis: Time-to-event modeling for churn, reliability, medical outcomes, and customer lifetime value.
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  Econometrics: Causal identification, instrumental variables, difference-in-differences, and time-series methods.
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  Causal Inference: Propensity score methods, synthetic controls, regression discontinuity, and natural experiments.
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  Portfolio & Risk: Portfolio optimization, risk modeling, and quantitative finance applications.

Churn Survival Modeling

When does a customer churn? How does behavior change the hazard rate? Cox models and Kaplan-Meier analysis with actionable segmentation.

Causal Impact of Policy Changes

Did a pricing change, feature launch, or market shift actually cause the outcome you observed? Rigorous causal methods to separate signal from noise.

Scientific Data Analysis

Publication-quality analysis for research teams, clinical trials, or regulatory submissions requiring defensible methodology.