Numbers you can defend in front of a board, a regulator or a reviewer.
Not every question needs machine learning. Many need a well-designed experiment, a correctly specified model and an honest confidence interval. Statistical rigour is what separates a persuasive chart from a reliable conclusion.
We build statistical tooling and analyses for product, finance and operations teams: A/B testing frameworks, survival and cohort models, forecasting with ARIMA and Prophet, and reproducible reporting.
How this differs from Data Science Solutions: Statistics answers 'is this true and how sure are we'; Data Science answers 'what will happen next' with deployed models.
Run trustworthy experiments and stop shipping changes that only look better.
Understand when and why customers churn with survival curves and cohorts.
Revenue and cash-flow projections with intervals leadership can plan around.
Control charts and anomaly thresholds for processes and services.
Chosen per project. We are vendor-neutral and will recommend what fits your constraints.
Turn the business question into a testable statistical statement.
Sampling, power, controls and the analysis plan agreed before data is collected.
Execute, check assumptions, quantify uncertainty.
Plain-language findings with the caveats that matter.
A methodology review of one live question, delivering a corrected analysis, a reusable template and a plain-language summary.
Yes. Independent review of methodology and conclusions is a common engagement.
Python and R with standard statistical libraries; results are reproducible from the raw data.
That is the goal. Every analysis ends in a plain-language summary and a decision recommendation.
Send a three-line brief. We reply within one business day.
A methodology review of one live question, delivering a corrected analysis, a reusable template and a plain-language summary.
