Data science services

We turn business questions into analyses, experiments and forecasting models that are reproducible, explained in plain language, and ready for your team to run without us.

What we build

Data science is most useful when it ends in a decision or a working system, not a slide deck. Our work usually falls into a few shapes:

• Exploratory analysis and data audits. Profiling a dataset to find out what it can support: coverage, gaps, duplicates, drift between sources, and the questions it can honestly answer.
• Experiment design and measurement. Defining metrics, sizing tests, and building the analysis code for A/B tests or other controlled comparisons, including the cases where a clean experiment is not possible.
• Forecasting and planning models. Demand, capacity, churn or workload forecasts with uncertainty ranges and a clear description of the assumptions behind them.
• Segmentation and scoring. Clustering, ranking and propensity models where a simple, explainable approach is preferred over a complicated one.
• Data pipelines for analysis. Cleaned, documented tables and scheduled jobs so the analysis can be repeated next month with new data.

When a problem turns out to need a production model, we continue into machine learning; when it needs deployment discipline, into MLOps.

How an engagement runs

1. Frame the question. We write down the decision the work supports, who will use it, and what a useful answer looks like. We also agree what would make us stop early.
2. Inspect the data. We review sources, definitions and quality, and report what is usable, what is missing and what needs a decision from your side.
3. Build a baseline. A deliberately simple method first, so every later improvement has something honest to be compared with.
4. Iterate with review. Short cycles with notebooks or reports you can read, challenge and rerun. Assumptions are written next to results.
5. Package and hand over. Code in your repository, pinned dependencies, a scheduled job if needed, and a walkthrough session.

Personal data and privacy

Analytics datasets often contain personal data in places nobody remembers. Before modelling we check what identifiers are present, whether they are needed, and whether pseudonymization or aggregation would do the job. If a dataset is not documented, a short data mapping and discovery pass is a sensible first step. For anything that feeds a language model, see our notes on privacy in LLM applications, and note that deletion requests can reach derived datasets too, as covered in data subject request automation. This is general engineering information and not legal advice.

Typical stack

We choose tools to fit your environment, and we generally work with:

• Python with pandas or Polars, NumPy, SciPy and statsmodels for analysis and statistics.
• scikit-learn for baselines and classical models; Jupyter or plain scripts for exploration.
• SQL against your warehouse or database, and dbt-style versioned transformations where they fit.
• Matplotlib, Plotly or your existing BI tool for reporting.
• Git, pinned environments and scheduled jobs (for example GitHub Actions or your orchestrator) so results can be reproduced.

Deliverables

• A written scope and a short findings document that states assumptions and limits.
• Analysis or modelling code in your repository, with tests for the data transformations that matter.
• Documented datasets or tables, including definitions of every metric.
• A handover session and a one-page guide to rerunning and updating the work.

When data science is the right first step

It fits when the problem is still loosely defined, when you are not sure a model is needed at all, or when the main risk is drawing the wrong conclusion from the numbers. It fits less well when you already know the model you need and mainly lack engineering capacity; in that case start with machine learning or Python and Node development. We will say so when we think a different service is the better starting point.

We also write down what the analysis cannot tell you. Observational data rarely proves cause and effect on its own, forecasts degrade as conditions change, and a metric that is easy to compute is not always the one that matters. Stating these limits up front keeps the results useful when someone reads them six months later.

Frequently asked questions

See all services.