Álvaro
Data Scientist
“In a climate this uncertain, Shakers gives you a layer of reassurance and confidence. What I like most, without a doubt, is the flexibility and getting to work on top-tier projects.”
Join ShakersShakers for expert talent
Work as a Data Scientist
Projects where the anticipated decision is yours: which metric you predict, with what validation criterion and which threshold it unblocks. Shakers connects you with companies that have the historical data and need the model.
The role reads in two places: a master's syllabus and the job ad asking for a PhD to train a first model. Neither tells you how to anticipate a real company's decisions project by project. Here is that.
- Higher-paying projects
- More continuity between projects
- Greater recognition for your experience
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6 months
Average length of a project with Shakers
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Product Designer
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Join ShakersWhat a data scientist decides on every project
A data scientist turns a company's historical data into anticipated decisions: defining the business question, exploring and preparing the data, training and validating predictive models in Python, translating the result into a decision threshold. Unlike the data analyst, they don't explain the past: they anticipate what comes. And they consume data someone else prepares: the pipeline belongs to the data engineer.
You decide the question the model answers and the metric that measures it: without a business definition, any model is an exercise. You train and validate with the available data, document what the model does when it hesitates and leave the threshold written. The pipeline is not yours: you consume prepared data and flag quality gaps at kickoff.
Pythonmachine learningpredictive modelsstatisticsSQLRpandasscikit-learnmodel validationA/B testingfeature engineeringtime series
Predictive modelsValidationMetric definitionPythonStatisticsA/B testing
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How Shakers works for talent
Create your profile
Tell us who you are, what you can do, the projects you want to work on and what your availability and rate are.
Get your certification
We certify your experience and your use of AI so companies understand and trust what you bring.
Match with projects
We connect you with well-paid projects that fit your expertise and your preferences.
Data and Analytics projects that live up to your expectations
Work on real projects, with teams that need your expertise, without wasting time looking for opportunities.
Projects that fit you
Work with companies that need your stack and experience to take their projects to production.
Opportunities that come to you
Our AI matching connects your profile with the projects that fit your preferences.
You build. Shakers handles the rest
Set your own terms based on your seniority. We manage contracts, payments and paperwork.
What a data scientist delivers on a Shakers project
The brief arrives with the decision
Shakers is hiring infrastructure: the project arrives with the decision the company wants to anticipate and its historical data, not a list of technologies.
A model you can audit
You deliver the model, the documented validation and the why of the threshold, including the scenario you dropped and why it stays out.
What happens when data shifts
You set the retraining, what happens when the distribution drifts and who answers for the result the day the model is wrong.
Decisions, not notebooks
Having shipped models someone used to decide and being able to tell which one failed, and what you did when validation contradicted you.
Simple or complex model
Choosing the model for the decision it unblocks, not its sophistication. Sometimes the honest answer is a regression, and defending it.
How far your brief goes
You consume prepared data; you don't build or run the pipeline. If the project needs infrastructure, you say so at kickoff and don't absorb it.
Frequently asked questions
Any other questions? Write to us and we will reply.
What 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. On the same base, the conversation you open differs.
Do you need a master's to work as a data scientist?
Statistics is learned through several doors: a master's, a bootcamp or your own projects. What the certification measures are real deliveries, not titles.
Is Python mandatory to be a data scientist?
In practice yes: it is the market's most requested skill. R and SQL complete the trade, and the exact stack follows the project's ecosystem.
How much does a freelance data scientist earn?
You invoice per project, and scope sets the band: model complexity, quality of the historical data and the decision threshold to accompany. You agree the budget before accepting.
Can predictive projects be done remotely?
Almost all of it: the historical data lives in the cloud and sessions with the company happen remotely. Only the definition phase occasionally asks for presence.
How do I join Shakers as a Data Scientist?
Create your profile, pass the certification process and join the collective: only 4 percent make it. Then you choose which predictive projects to work on.
The day to day of the trade
The model nobody validated, the metric nobody defined well and the result that stays in a slide deck. The trade is learned delivering decisions: fewer school notebooks, more thresholds the company uses.
The market asks for the role, not the discipline
1,972 live UK tech vacancies mention data scientist, according to Shakers' market analysis (n=39,811, September 2026). Titles confuse; delivery decides.
The barrier is judgement, not the degree
Libraries are learned fast; judgement is not. Which metric gets predicted, how it's validated and what happens when the model hesitates: that is what our certification measures.
How people arrive from other trades
The frequent door is the analyst who started predicting: from SQL and dashboards to the first validated model. Another comes from statistics: whoever handles data with Python has half the battle won.
Roles that cross a predictive project
Four roles from the collective cross your path on a predictive project, from the one preparing data to the one serving it to the product.
Data Analyst
Where your model starts anticipating, their reporting ends up explaining what happened: same base, different conversations.
Data Engineer
Builds the pipeline that prepares your historical data and carries the model to production: without reliable data, prediction doesn't hold.
Backend Developer
The APIs exposing your model: when prediction must answer in real time, they integrate it into the product.
Python Expert
The scripts moving and cleaning the historical data between systems when standard ETL doesn't reach the project's volume.
The boundaries between these roles are agreed on each project: the title does not set them.
Take the next step with Shakers
Want projects where you predict with judgement?
Pass Shakers' certification and choose which predictive projects to join.
