Consulting
Available for Consulting
I work with teams who want to move beyond dashboards and toward decisions — using Bayesian methods, causal reasoning, and rigorous experimentation to extract real insight from messy data. If your team is wrestling with any of the problems below, get in touch.
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prior → posterior
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P(θ|D) ∝ P(D|θ) · P(θ)
Bayesian Statistics
Probabilistic models that quantify uncertainty honestly ranging from hierarchical mixed-effects models to latent variable structure. Useful when you need to pool information across groups, handle small samples, or communicate confidence ranges rather than point estimates.
Model Diagnostics · People Analytics · PyMC · GLMs · SEMs · Survival Analysis
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↓ ↓
[X] ──────→ [Y]
cause effect
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do(X=x) ≠ see(X=x)
Causal Inference
Identifying what actually drives outcomes, not just what correlates with them. I work with structural causal models, quasi-experimental designs, and observational data to support decisions where you can't run a clean experiment.
DAGs · Instrumental variables · Difference-in-differences · Discrete Choice Models · Propensity scores · Experiment Design
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║ y ~ Normal(μ, σ) ║
║ μ = α + β·x ║
║ σ ~ HalfNormal ║
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Training & Workshops
Hands-on sessions for data science and analytics teams — from half-day introductions to multi-day workshops with worked examples in your domain. Past topics include Bayesian workflow, causal inference, and probabilistic programming with PyMC.
In-person or remote · Beginner to advanced