Data scientist — what the job involves and how to become one in Poland

A data scientist turns data into decisions: they frame a business problem, prepare the data, build and evaluate statistical or machine learning models, and explain the results to the people who act on them. To become a data scientist you need Python, SQL, statistics and a few projects built on real data. In Poland, many people enter the profession through a data analyst role first.

This guide covers what the job looks like, how it differs from related roles, a step-by-step learning plan, salaries and what the Polish market looks like today.

What does a data scientist do?

A typical project looks like this:

1. Understanding the problem — what do we want to predict or explain, and how will we measure success (for example, reducing customer churn by a few percent)?
2. Data — pulling it from databases (SQL), warehouses or APIs, cleaning it, joining sources, handling gaps and errors.
3. Exploration — charts, descriptive statistics, testing hypotheses.
4. Modelling — from simple regression to gradient boosting or neural networks; choosing metrics and validation.
5. Communication — presenting results, limitations and recommendations in business language.
6. Deployment and monitoring — in mature teams the model goes into production and the data scientist keeps an eye on its quality.

In practice a large share of the time goes into preparing data rather than modelling. That is normal — and worth knowing before you choose the profession.

Examples of data science projects

• E-commerce — product recommendations, demand forecasting, spotting customers who are about to stop buying.
• Banking and insurance — credit risk scoring, fraud detection, pricing.
• Logistics and manufacturing — route optimisation, predicting machine failures from sensor data.
• Healthcare — supporting image analysis, forecasting hospital occupancy.
• Customer service — automatic ticket classification and sentiment analysis with language models.

The common denominator: a model only makes sense if someone makes a different decision because of it. A data scientist who asks the business good questions is worth more than one who knows the most algorithms.

Data scientist vs data analyst, data engineer and ML engineer

Not sure which of these fits you? Answer three questions (the linked role guides are in Polish):

Data scientist skills: must-have and nice-to-have

How to become a data scientist: a plan from scratch

The timeline below assumes 10–15 hours of study per week. It is faster if you already code or have worked with data in Excel.

Realistically, going from zero to a first data job takes 9–15 months.

Step 1: Learn Python and SQL

Start with Python, but do not try to learn everything. Data structures, functions, file handling and pandas are enough at first. Learn SQL in parallel — it appears in almost every data science job ad, and recruitment often includes a separate SQL test. In the No Fluff Jobs report “Kariera w IT 2026” (summarised by Antyweb on 3 July 2026), Python appeared in 21.6% of IT job offers in Poland in the first half of 2026 and SQL in 20.3%, making them the two most requested technologies.

Step 2: Learn statistics through practice

Rather than reading a textbook cover to cover, work with data: compare the mean and median of salaries in a public dataset, test whether a difference between two groups is significant, fit a simple regression and interpret the coefficients. Knowing when a result is just chance matters more than knowing ten algorithms.

Step 3: Move into machine learning

scikit-learn lets you go through the whole process: splitting data, feature preparation, training, cross-validation and choosing metrics (accuracy is not enough for imbalanced data). Start with linear and logistic regression, decision trees and random forests, then gradient boosting. Leave deep learning for later — most business problems are solved with classic ML.

Step 4: Build a portfolio

Two or three polished projects, ideally from different domains, are enough. Deploy one of them as a simple API or app to show you understand what happens after the notebook.

Step 5: Apply broadly and prepare for tasks

Data science recruitment usually includes an SQL test, a take-home analysis and a conversation about a project. Practise explaining your choices: why this metric, why this model, what you would do differently. Apply not only for “data scientist” roles but also for data analyst, BI analyst and internships in data teams (our internship guide is in Polish). Our article on the CV format Polish recruiters expect helps with the application – remember the GDPR clause for a CV in Poland – and you can add your anonymous CV so recruiters can find you.

Certifications in data science

Certifications matter less than a portfolio in data science, but they can help you pass CV screening at large companies:

An overview of which IT certificates are worth having is available in Polish.

Data scientist salary in Poland

Salary ranges from Data & AI job offers on LITJobs:

There are not enough offers with salary ranges in this category on LITJobs yet to show a median — we show it from 3 ranges (see our methodology). Until then, rely on the reports cited in the text.

Bulldogjob does not split the Data Science/ML/AI category by seniority, so the chart shows its median next to the medians for juniors and seniors across all of IT:

The highest rates go to people who combine modelling with deployment (MLOps) and domain expertise. B2B and employment figures are not directly comparable: B2B contractors pay their own social security and usually have no paid leave. For the full picture, read our comparison of B2B and employment contracts in Poland.

Junior data scientists in Poland: the market today

Data and BI is one of the largest categories in the Polish market: in the No Fluff Jobs report “Kariera w IT 2026”, Data & Business Intelligence accounted for 12% of IT job offers, second only to backend (17.5%). The same report says that between January and May 2026 there were on average about 70% more job ads than a year earlier, yet each junior offer attracted 47 applications on average. Roles titled “junior data scientist” are rare — companies more often hire juniors as analysts or data engineers. Current entry-level openings are listed under junior IT jobs.

What works:

• entering through data analysis or BI and moving to modelling after a year or two;
• moving internally — if your company has a data team, propose a project on your own team’s data;
• combining data science with domain knowledge (accounting, logistics, healthcare);
• working with language models — more and more projects involve text classification, semantic search and LLM evaluation.

Remote work in data science

Data work suits remote setups well. The main limitation is security: in banking, insurance and healthcare, sensitive data is often accessible only from the company network, so hybrid work is common. Juniors usually spend more days in the office during their first months. Fully remote openings are collected under remote IT jobs.

Data scientist jobs

Latest offers in the Data & AI category:

Data science course and funding options in Poland

LearnIT’s Data Science course covers Python, data analysis and machine learning through projects. If you live and work in Poland, part of the cost can often be covered by public funding; the documents and deadlines are described step by step in our guide to funded IT courses (in Polish).

Related career paths

• How to become a DevOps engineer — infrastructure for MLOps and model deployment.
• Cybersecurity specialist — protecting systems and data, including AI systems.
• Data engineer (in Polish) — building the pipelines and warehouses models depend on.
• Data analyst (in Polish) — the most common entry route into data science.