Data Scientist.

Finds the answers in your data that make decisions better.

Remote & on-site DACH-wide
Data Scientist — enquire

A Data Scientist answers business questions with data: Which customers are churning, and why? What is driving our quality problems? Where are we losing margin? They combine statistical methodology with business understanding — and communicate results in a way that turns them into decisions.

In an AI context, the role is the foundation: before models are built or agents are trained, someone has to assess the data situation honestly, test hypotheses and calibrate expectations. Many failed AI projects would never have started — or would have started differently — given two weeks of clean data analysis beforehand.

01

Exploratory data analysis before AI projects

Can your data situation support the planned initiative? An honest assessment of quality, coverage and bias — before budget is committed.

02

Customer and churn analyses

Who cancels, who buys, what drives both? Segmentations and driver analyses with actionable recommendations.

03

Experiment design & A/B testing

Measure interventions properly instead of falling for correlations — test design, evaluation, interpretation.

04

Management reporting with substance

From gut-feeling dashboards to reliable metrics with clear methodology and documented assumptions.

Core competencies
Statistics & hypothesis testingExploratory analysisSQLPython (pandas, statsmodels)Visualisation & storytelling
Tools & frameworks
JupyterTableau/Power BIdbtExperimentation platformsGit
Plus factors
Causal InferenceIndustry domain knowledgeHandover to ML engineering
Tell us about an analysis whose result the client did not like.
Integrity: Data Scientists who only deliver desired results are expensive self-deception.
How do you distinguish correlation from causation in practice?
The classic on which expensive bad decisions hinge — the answer should demonstrate concrete methods and humility.
How do you communicate uncertainty to non-statisticians?
Confidence intervals that nobody understands change no decisions — the ability to translate is a core skill.
When is a simple evaluation better than a model?
Seniority shows in parsimony: anyone who answers everything with ML has not understood the business problem.
What does a Data Scientist cost as a freelancer?
Based on our market observations, hourly rates in the DACH region in 2026 range between 95 and 140 euros, depending on statistical depth and industry experience.
What is the difference between a Data Scientist and a Data Analyst?
The boundaries are fluid — as a rule of thumb: the analyst describes what happened; the Data Scientist tests hypotheses, models relationships and quantifies uncertainty. For many questions in mid-sized companies, combining both in one person is ideal.
Should we hire a Data Scientist or an ML Engineer first?
In most cases the Data Scientist first: they assess whether your data situation and question can support an ML project at all. This protects against the most expensive mistake — models built on unsuitable data.
How quickly does a Data Scientist deliver initial insights?
A focused exploratory analysis typically delivers initial reliable answers after two to three weeks — provided data access is in place from day one.

Looking for a Data Scientist?

Tell us about your project — within one business day you’ll get an honest assessment of availability, day rate and alternatives.

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