We work on the questions that decide budgets: what actually caused the lift, how much of next quarter's plan is real, which customers are worth acquiring, and what a price change does to margin. Every analysis ships as reproducible code with stated uncertainty and a plain-language recommendation, so decisions rest on evidence rather than the loudest opinion in the room.
Most teams are not short on data or charts. They are short on answers: whether the campaign actually caused the lift, how much of next quarter's plan is real, which lever moves retention, and how wrong the estimate could be.
We work the question end to end. Frame the decision, pick a method that fits the data you actually have, quantify the uncertainty, and hand over reproducible code plus a recommendation someone can defend in a meeting.
1 wk
Decision framing before any modeling starts
Ranges
Every estimate carries an interval, not one number
Reproducible
Analyses ship as versioned code you can re-run
Plain English
A decision memo, not a forty-slide deck
Six engagement types that cover most of what teams need in order to measure, forecast, and price with confidence.
The measured effect and its confidence, so a decision does not rest on which bar looks taller.
Where revenue compounds over time, and where it quietly leaks a few weeks after signup.
Groups defined by what customers actually do, each tied to one specific action.
Tests designed to answer the question before you spend the budget, and honest readouts when the result is not what anyone hoped for.
Demand, revenue, and capacity models that finance and operations can plan against, with error bars instead of false precision.
Where growth actually comes from: which cohorts compound, which churn quietly, and which acquisition spend pays for itself.
What a price or packaging change really does to volume and margin, modeled before you learn it from a churn spike.
When the question is how much and where, not whether: inventory levels, staffing, routing, and risk under uncertainty.
One definition of revenue, one of active user. Shared metrics and models so debates are about strategy, not whose number is right.
A typical engagement runs six to ten weeks, and the first week is deliberately spent not modeling.
1 week
We interview the people who will act on the result, define the decision and the threshold that would change their mind, then audit whether your data can support it.
2–3 weeks
Cleaning, sanity checks, and a deliberately boring baseline. A naive benchmark is the only honest way to tell whether anything clever earns its complexity.
3–4 weeks
The method the question deserves, whether that is an experiment readout, a causal estimate, or a forecast, with backtests and sensitivity analysis attached.
Ongoing
A recommendation in plain language, then the plumbing: scheduled refreshes, monitoring, and a review cadence so the answer does not quietly go stale.
Decisions backed by measured effects rather than dashboard intuition
Forecasts with honest error bars that planning teams can commit to
A clear read on which levers move revenue, retention, and margin
Analyses your team can re-run, extend, and audit without us
The tools we reach for on Data Science work, picked for the problem in front of us and for the team who inherits the code.
Where the questions get taken apart
Forecasts and causal work with stated uncertainty
One definition of every metric
Findings people can act on without you present
Dashboards report what happened and are good at it. Data science answers why it happened, what happens next, and what to do about it, which takes experiment design, causal methods, and stated uncertainty. In practice we often retire a few dashboards and replace them with one recurring readout that answers a specific question.