From the first dataset to the recommendation that lands on a CFO's desk, we extract the patterns, quantify the uncertainty, and ship the analysis as a decision, not a deck.
The Data Science practice turns messy operational data into something a non-statistician can act on. Statistical analysis, predictive modeling, and the data engineering it takes to make the analysis reproducible. Notebooks are not the deliverable; better decisions are.
Every engagement starts where the data does: cleaning, profiling, and understanding before a single model is fit. We benchmark approaches, communicate the trade-offs honestly, and deploy whatever makes the call faster, cheaper, or more accurate. Customer segmentation, demand forecasting, causal analysis, experimentation, the thinking does not get outsourced.
Whatever the objective, customer experience, operations, market opportunity, the output comes with calibrated uncertainty. You will know when to trust the answer and when to gather more data.
Six disciplines we draw from on every engagement, from the first exploration through to the production-grade artifact that lives in your business.
Profiling, distributions, missingness, outliers, leakage, drift. Before a single model is fit, we know exactly what's in your data, what's wrong with it, and what story it tells. Half of every project lives in this phase, and that's deliberate.
Linear, logistic, mixed-effects, generalized linear, Bayesian. When the question is "is this real?" or "by how much?", not "predict the next one", we reach for the right test, report the right interval, and resist the urge to p-hack.
From demand forecasts to churn scores, lifetime value to defect prediction. Calibrated probabilities, prediction intervals, and clear baselines, so you know when to act on a number and when it's noise. Reproducible, versioned, deployable.
Analysis is only as good as the pipeline feeding it. We build the data plumbing, SQL, dbt, Spark, Airflow, that turns raw operational data into reliable, governed datasets your analysts and models can both depend on.
Randomised trials, difference-in-differences, propensity scoring, synthetic controls. When you need to know whether something caused something, not just correlated with it, we design the experiment, run the analysis, and write up what's defensible.
The chart depends on the audience. We build dashboards, narratives, and one-pagers calibrated to the person making the decision, an executive briefing reads differently from an operations dashboard, and we treat both as deliverables, not afterthoughts.
A representative slice of the questions our data science work has actually answered, and the decisions those answers unlocked.
A subscription business knew it had churn but couldn't tell why. We built a churn model on usage telemetry, surfaced the top three behaviors that predicted cancellation, and ran a controlled experiment that proved a targeted intervention reduced 30-day churn measurably for the at-risk cohort.
A multi-store retailer was over-stocking everywhere and under-stocking in the wrong places. We built a hierarchical forecast across SKU, store, and week, with calibrated intervals and a feedback loop that incorporated promotion calendars and weather. Inventory cost dropped without raising stock-outs.
A consumer brand was spending across paid, owned, and earned channels with no defensible attribution. We built a mixed-media model with adstock and saturation curves, validated against geo holdouts, and produced a budget reallocation that the CFO would actually sign.
Data science goes wrong in predictable ways, over-fitted models, p-hacked results, charts that flatter the author. These five operating rules are how we stay out of those traps on every engagement.
We start with the decision the model is supposed to inform. If the decision can already be made well from a dashboard or a SQL query, we say so and don't build a model. If it can't, we benchmark the value of a better prediction in dollars or operational impact before committing to the work.
A written analytical report with the methodology, code in your repository, reproducible notebooks, a deployment if a model was the right answer, and a slide deck designed for a non-statistician audience. The data and findings sit in your environment; we don't host your data.
That's most of what data science work actually is. We profile, clean, and document the data quality before modeling, and we tell you explicitly which decisions are well-supported by the data and which aren't.
Every estimate ships with a calibrated confidence interval, not a single point estimate. For predictive models we report calibration curves; for inferential work we report effect sizes with confidence intervals; for A/B tests we report power and minimum detectable effect alongside the result.
We work inside whatever boundary your regulatory profile requires: your VPC, your on-prem, or your secure enclave. For healthcare we sign a BAA and operate as a covered-entity sub-contractor. PHI never leaves the boundary you control.
Bring us the question, the messy dataset, and the decision waiting on it. We'll come back with a one-page analysis plan: what we'll measure, how we'll know, and when you'll have the answer.