How is this different from the BI dashboards we already have?

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.

How is this different from your AI and machine learning work?

Machine learning ships predictions into software: a model serving traffic behind an API. Data science produces decisions for people: a measured effect, a forecast, a pricing recommendation. Plenty of clients need both, so we are explicit about which one your question actually calls for.

We do not have enough traffic for A/B tests. What then?

Then we use quasi-experimental designs. Geo or store-level holdouts, difference-in-differences, synthetic control, and Bayesian methods with sensible priors all work at lower volume. We also right-size the ambition: with thin data, the honest output is a range and a recommendation to test small, not a precise-looking number.

Will you work inside our warehouse and tooling?

Yes. We work in your warehouse, your dbt project, and your repository, and we leave the analysis versioned there. If the data foundation itself is the blocker, we say so in week one rather than modeling on sand.

What do we actually receive at the end?

A short decision memo with the recommendation, the effect size, and the uncertainty; a reproducible repository with code and data lineage; and whatever automation the answer needs, whether that is a scheduled refresh, a dashboard, or an experiment framework your team runs itself.

Data Science

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.

Start a project

A dashboard tells you what happened. It rarely tells you what to do

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

What's included

Experiments, forecasts, and causal analysis that answer the business questions dashboards leave open.

Experiment design, A/B testing & sequential analysis

Causal inference: incrementality, geo-lift & difference-in-differences

Demand, revenue & capacity forecasting with uncertainty

Customer analytics: LTV, cohorts, churn drivers & segmentation

Pricing, elasticity & scenario modeling

Analytics engineering, metric trees & decision reporting

Four habits that make an analysis worth trusting

None of these are exotic. Skipping them is why so much analytical work gets politely ignored.

Frame the decision

We start from the decision and the cost of getting it wrong, which usually changes the question, the metric, and how much precision is worth paying for.

Quantify the uncertainty

Point estimates hide risk. We report intervals, run sensitivity checks, and say plainly where the data is too thin to support a confident call.

Establish causality

Correlation is cheap. We design experiments where you can run them, and use quasi-experimental methods such as geo-lift and difference-in-differences where you cannot.

Land it in the workflow

An answer stranded in a notebook expires. We put the readout where the decision gets made and schedule it so it stays current without a heroic effort.

Analysis that changes a decision, not just a slide

Six engagement types that cover most of what teams need in order to measure, forecast, and price with confidence.

Experiment readouts

The measured effect and its confidence, so a decision does not rest on which bar looks taller.

Cohort retention

Where revenue compounds over time, and where it quietly leaks a few weeks after signup.

Behavioral segments

Groups defined by what customers actually do, each tied to one specific action.

Experimentation & causal inference

Tests designed to answer the question before you spend the budget, and honest readouts when the result is not what anyone hoped for.

A/B and multi-arm test design
Power analysis and guardrail metrics
Geo-lift, difference-in-differences, synthetic control

Forecasting & planning

Demand, revenue, and capacity models that finance and operations can plan against, with error bars instead of false precision.

Demand, revenue and capacity models
Scenario and what-if planning
Backtested accuracy with confidence intervals

Customer & revenue analytics

Where growth actually comes from: which cohorts compound, which churn quietly, and which acquisition spend pays for itself.

LTV, cohort and retention curves
Churn drivers and propensity scores
Segmentation that maps to specific actions

Pricing & elasticity

What a price or packaging change really does to volume and margin, modeled before you learn it from a churn spike.

Willingness-to-pay research
Elasticity and margin modeling
Packaging and discount policy tests

Optimization & simulation

When the question is how much and where, not whether: inventory levels, staffing, routing, and risk under uncertainty.

Inventory, staffing and routing models
Monte Carlo risk simulation
Constraint-based scenario search

Analytics engineering & metric layer

One definition of revenue, one of active user. Shared metrics and models so debates are about strategy, not whose number is right.

Metric trees and shared definitions
dbt models and a semantic layer
Self-serve reporting people actually trust

From a fuzzy question to a decision you can defend

A typical engagement runs six to ten weeks, and the first week is deliberately spent not modeling.

Stage 01

1 week

Decision framing

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.

Decision statement & success metric
Data feasibility review
Method shortlist with tradeoffs

Stage 02

2–3 weeks

Exploration & baseline

Cleaning, sanity checks, and a deliberately boring baseline. A naive benchmark is the only honest way to tell whether anything clever earns its complexity.

Exploratory findings memo
Baseline estimate or model
Documented data caveats

Stage 03

3–4 weeks

Modeling & validation

The method the question deserves, whether that is an experiment readout, a causal estimate, or a forecast, with backtests and sensitivity analysis attached.

Validated model or test readout
Backtest & sensitivity results
Reproducible analysis repository

Stage 04

Ongoing

Decision & automation

A recommendation in plain language, then the plumbing: scheduled refreshes, monitoring, and a review cadence so the answer does not quietly go stale.

Decision memo & readout session
Scheduled pipeline or dashboard
Review cadence & ownership plan

Outcomes we optimize for

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

What we build it with

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.

Analysis

Where the questions get taken apart

Python
pandas
Polars
NumPy
Jupyter

Modelling

Forecasts and causal work with stated uncertainty

scikit-learn
PyTorch
Keras
MLflow

Warehouse

One definition of every metric

Snowflake
BigQuery
dbt
DuckDB

Delivery

Findings people can act on without you present

Streamlit
Plotly
Metabase

Questions we get in the first call

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.

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Let's build something that lasts

Tell us about your project and we'll get back to you within 12 hours with next steps.

Start a project See our work