Data science engagements that produce real business decisions — exploratory analysis, A/B test design, causal inference, and dashboards your leadership team actually uses. Statistics and storytelling, applied with rigor.
A lot of data science output is technically correct and commercially useless. We focus on the questions where the answer changes a decision: which product to invest in, which customer segment to prioritize, whether the experiment really moved the needle. The deliverable is a decision, not a notebook.
Discuss your project ↗Every engagement gets shaped to fit, but these are the building blocks we rely on.
Deep dives into customer behavior, product usage, and operational data. Findings communicated as decisions, not just charts.
Proper experiment design — power analysis, randomization, guardrails, and statistical rigor. Tests that produce trustworthy answers.
When randomized tests aren't possible, we apply matching, instrumental variables, and difference-in-differences to isolate causal effects.
Looker, Metabase, or Mode dashboards that answer the questions leaders actually ask. Built around decisions, not metrics.
Retention curves, funnel diagnostics, and cohort behavior analysis to identify where your product actually creates and loses value.
Revenue forecasts, capacity planning, and scenario analysis. Numbers that hold up in board meetings.
Two decades of engineering practice, sharpened by the realities of production AI.
Every analysis starts with the decision it informs. If no decision changes, we say so up front.
Proper statistical practice, communicated in plain English. Confidence intervals matter; p-hacking doesn't.
Memos, briefs, and decision documents — not just dashboards and notebooks. The people who need to act, can.
We work with your existing dbt models, warehouse, and BI stack. We don't recreate infrastructure to serve analyses.
Let's discuss how this fits your business. We reply within one working day.
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