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AI, Data & Emerging Technology · Data science

Data science that answers the question you asked

We take a concrete business question, such as why customers leave or which channel really drives sales, and answer it with careful analysis, experiments and models, delivered as clear findings and reusable code.

  • Exploratory analysis
  • Experiment design
  • Customer segmentation
Start your projectHow we work

Overview

How analysis turns into a decision

Data science work starts with a question someone needs answered: why did retention fall, which channel actually drives new customers, did the pricing change help. The analyst’s job is to translate that question into a measurable comparison, find data that can answer it fairly, and be clear about how confident the answer is. That last part is often skipped, and it matters most.

The main trade-off is rigor against speed. A quick descriptive cut can be ready in days but may mistake correlation for cause. A controlled experiment or careful causal method takes longer and needs the right data, yet gives an answer you can defend. We match the method to the stakes of the decision and say plainly what each approach can and cannot show.

Good analysis is reproducible, states its assumptions and ends with a recommendation, not just charts. Another analyst should be able to rerun the work and reach the same numbers. Your team should also come away knowing what was not tested and which follow-up questions are worth asking in the next round of work.

Who it’s for

Built for teams like yours

  • 01

    Leadership teams facing a choice

    Executives weighing a pricing change, market entry, product cut or budget shift who want an independent, evidence-based read before committing resources. We have no stake in which option you choose.

  • 02

    Marketing and growth teams

    Teams that run campaigns and product tests but are unsure which results are real, how to size experiments, or how to attribute outcomes across channels.

  • 03

    Companies without analysts

    Growing businesses that have plenty of data in their tools but nobody with the time or training to analyze it properly and turn it into answers.

Why it matters

Evidence instead of opinions

Many companies have plenty of data and still argue about what it means. Data science brings method to those debates: defining the question precisely, checking data quality, separating correlation from cause, and stating how confident the answer is. Good analysis often saves money by showing what not to build or fund.

We write up findings in plain language for decision makers and keep every notebook and query reproducible, so your analysts can rerun, question and extend the work long after we hand it over.

Every engagement includes

  • Question framingwe agree on the decision, the metric and what evidence would change your mind.
  • Data access & quality reviewsources connected and checked for gaps, duplicates and bias.
  • Analysis & modelingmethods matched to the question, with assumptions written down.
  • Findings reporta plain-language summary with charts, confidence levels and recommendations.
  • Working sessiona walkthrough with your team to challenge results and plan next steps.
  • Code handovernotebooks, SQL and datasets delivered in your repository with documentation.

Features

Methods we apply

  1. 01

    Exploratory analysis

    Profiling and visual exploration that reveal data gaps, outliers and early patterns worth testing.

  2. 02

    Experiment design

    A/B and multivariate tests with proper sample sizes, guardrail metrics and honest readouts.

  3. 03

    Customer segmentation

    Clustering on behavior and value so marketing and product teams target groups that really differ.

  4. 04

    Attribution and causal analysis

    Methods like difference-in-differences and uplift modeling to estimate what actually caused a change.

  5. 05

    Statistical modeling

    Regression, survival and time-series models that quantify what drives an outcome and how certain we are.

  6. 06

    Reproducible notebooks

    Python or R code in versioned notebooks so any result can be rerun and checked later.

In practice

Questions we help answer

  • Why customers leave

    Cohort and survival analysis of customer history shows when churn happens, which segments are most affected and which behaviors early in the relationship tend to come before cancellation. Findings point to the moments worth intervening.

  • Whether a change worked

    Before-and-after comparisons are replaced with proper experiments or quasi-experimental methods, giving a defensible estimate of what a price, feature or process change actually did. We also state how large an effect the data could reliably detect.

  • Which customers to target

    Clustering and profiling group customers by behavior and value, producing segments with clear descriptions that marketing and sales can use in campaigns and messaging. Each segment comes with size, value and a short behavioral profile.

  • Where budget is best spent

    Marketing mix and incrementality analysis estimate how much each channel contributes beyond what would have happened anyway, informing next quarter’s allocation. Results come with uncertainty ranges and the assumptions behind them, so budget owners can see how firm each estimate is.

Process

How we work

  1. 1

    Question workshop

    We turn a broad concern into specific, answerable questions, agree on the metrics and comparison groups, and note which decision each answer will inform. Questions with no linked decision are set aside for later.

  2. 2

    Data profiling

    Relevant tables are pulled, joined and checked for gaps, duplicates and definitional differences, with any limits on what the data can show written down early. Data owners on your side confirm definitions before any analysis begins.

  3. 3

    Method selection

    We choose between descriptive analysis, experiments, regression, causal inference or segmentation based on the question and data, and explain the choice before running it. Simpler methods win when they answer the question honestly.

  4. 4

    Analysis and checks

    The analysis is run in version-controlled notebooks, then stress-tested with sensitivity checks and alternative specifications so conclusions do not rest on one assumption. Results that change under reasonable alternatives are reported as uncertain.

  5. 5

    Readout

    Findings are presented in plain language with uncertainty stated, followed by a discussion with your team and a written recommendation on what to do next. Notebooks and data are handed over so the work can be rerun.

Deliverables

What you receive

  • Agreed question and metric definitions
  • Data quality and limitations memo
  • Version-controlled analysis notebooks
  • Plain-language findings report
  • Charts ready for internal presentations
  • Experiment design or sizing plan
  • Recommendations with stated confidence

Tools & methods

Analysis

  • Python
  • R
  • SQL
  • pandas
  • statsmodels
  • Jupyter

Methods

  • A/B testing
  • causal inference
  • survival analysis
  • clustering
  • Bayesian modeling

Data sources

  • Snowflake
  • BigQuery
  • GA4
  • HubSpot
  • Salesforce

FAQ

Frequently asked questions

Anything else about Data science? Ask us directly.

  1. Business intelligence reports what happened, usually through recurring dashboards. Data science investigates why it happened and what is likely to happen next, using statistics, experiments and models. The two work together: a data science finding often becomes a new metric that BI then tracks every week.

Let’s work together

Have a project in mind?

Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.

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