Mateo
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Shakers for companies
Work with a senior Data Scientist
A certified data scientist who turns your historical data into anticipated decisions: defining the metric that matters, training the model that anticipates it and leaving the validation documented before anything reaches production.
Senior tech talent for your predictive project, starting in days on the data you already have.
- Certified talent in AI and agent skills
- Meet up to 3 AI Builders in 72 h
- Flexible collaboration models
+600
European clients across different industries
Top 4%
We work with the top 4% of certified talent in our community
72 hours
Meet 3 certified candidates in under 72 h
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Our clients' satisfaction on Trustpilot
The talent mix your company needs
At Shakers you will find AI Engineers, Data Scientist and specialists across Tech, Product and Data
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DevOps Engineer
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Backend Developer
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Security Engineer
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Data Scientist: what this role brings to your project
A data scientist turns a company's historical data into anticipated decisions: they define the business question, prepare and explore the data, train and validate predictive models and translate the result into a measurable decision.
They work in Python with machine learning libraries over data someone else prepares: the data engineer builds the pipeline and the analyst explains what happened. The line is simple: the scientist predicts, the analyst explains.
Pythonmachine learningpredictive modelsstatisticsSQLRpandasscikit-learnmodel validationA/B testingfeature engineeringtime series
One provider. One contract. Full traceability
How Shakers works for your company
Post your project
Tell us about the project, its scope, the expertise you need and when you want to get to work.
You get three options
We match your brief against certified talent and introduce the three AI Builders who fit best.
The last word is yours
Meet the three specialists and choose who you collaborate with; we handle the rest.
Why work with Shakers
The advantages of adding this profile with Shakers
The metric gets defined before the model
The project starts with the business question: what is anticipated, for which decision, and with what success criterion.
Models with documented validation
Every delivery includes how it was validated, on which data it was tested and what it does when it hesitates: no black boxes.
Prediction on the data you already have
Python and statistics over your current historical data: no migrations and no new infrastructure before testing the idea.
Predictive capacity without a data team
Data science by project, no hiring process: certified talent joining with your current access and systems.
From prototype to decision threshold
The model arrives with its threshold: what happens when it predicts yes, what when it hesitates, and who answers for the result.
The analyst and engineer know their place
Boundaries with the analyst and the engineer are agreed at kickoff: the scientist consumes prepared data, never absorbs pipelines.
Frequently asked questions
Any other questions? Our team will answer them.
Tell us your data projectWhat is the difference between a data scientist and a data analyst?
The analyst explains what already happened with the data being measured; the scientist trains models that anticipate what comes. Many projects start with the analyst and grow into the scientist.
How much does a Data Scientist engagement cost?
Scope sets the price: model complexity, quality and volume of the historical data and the decision threshold to accompany. The budget closes per project before starting.
What does a data scientist need to start?
Access to the historical data, one session to define the business metric and the model's success criterion. With that, the first exploration with real data arrives in days.
External data scientist or in-house team?
The external fit is a bounded predictive project with existing historical data; the in-house team, when models become part of the product. Many start external and decide later.
Can predictive projects be done remotely?
Almost all of it: the historical data lives in the cloud and sessions with the team happen remotely. Only the definition phase occasionally asks for presence.
How long until there is a first validated model?
With access and one scoping session, the first prototype with real data arrives in days. Full validation and the decision threshold consolidate in phases.
What gets delivered in the first weeks
The definition of the metric to predict, the first model with documented validation and the agreed decision threshold: what happens when the model says yes, and what when it hesitates.
1,972 live UK vacancies mention data scientist
1,972 live UK tech vacancies mention data scientist, according to Shakers' market analysis (n=39,811, September 2026). The role leads the Data family, and Python is the most requested skill.
Where the data scientist ends
They consume prepared data and return models: they neither run the pipeline nor explain the past. If data isn't arriving with quality, the role is data engineer; if the past needs explaining, data analyst.
The market's titles confuse on purpose
The same profile answers to data scientist, ML engineer or advanced analyst. What defines the role isn't the title: it's the decision their model unblocks.
Connect with AI specialists to build your next project
Find specialists in Tech, Product, Data to complement your team
Data Analyst
Explains what happened with the data being measured: your model predicts on the same base and with the same KPI definition.
Data Engineer
Builds the pipeline that carries your model to production: without reliable data on cadence, prediction doesn't hold.
Backend Developer
The APIs exposing your model to the product: when prediction must answer in real time, they serve it.
Python Expert
The scripts moving and preparing the historical data when standard ETL doesn't reach the project's volume.
The AI talent you need Certified by Shakers
We introduce you to three ready_to_build AI Builders in 72 hours.
