Paweł Krakowski
Senior Data Scientist
Writing about technology trends, career development, and the Polish tech landscape.
7 artykuły opublikowano
Artykuły autora Paweł Krakowski
29 października 2025
The Salary Mirage When AI Gets Pay Wrong
Ask any AI what a role should pay and you’ll get something crisp: clean ranges, tidy percentiles, confident tone. For HR on deadline and teams chasing parity, it feels like progress.
15 października 2025
Evolving Data Scientist in the Age of AI
Once upon a time, being a data scientist meant hand-wrangling data and tuning models line by line. Today, AI1 handles much of that work: AutoML2 assembles pipelines, agents generate code, and LLMs3 narrate results in plain English.
8 października 2025
The Special Relativity of Seniority: Part 2
In Part 1, we traced careers in their own time, advancing by scope, accountability, and outcomes rather than years. That was the traveler’s view: motion measured by impact, not tenure.
1 października 2025
The Special Relativity of Seniority: Part 1
In relativity, each traveler traces a path through spacetime. Along that path, the traveler’s clock measures proper time, the time personally experienced. Observers in other frames may disagree about how much time has passed, but the traveler’s clock
24 września 2025
From Pipelines to Paychecks: Poland’s Data Engineers in 2025
Data engineers have moved from the back room to the boardroom. In Poland’s 2025 tech scene, they are the builders of AI-ready infrastructure, wiring cloud, streaming, and governance so models don’t just run, they scale. From fintech and telecom to ga
17 września 2025
Cypress vs Selenium in 2025: Popularity, Pay, and the Future of QA
Poland has firmly established itself as a key hub for IT and software quality assurance in Europe, with QA automation at the center. Companies are investing heavily in automated testing to accelerate delivery, ensure reliability, and keep pace with c
10 września 2025
From Hype to Hires, AI Engineers Rise in Poland’s Tech Scene
Large Language Models (LLMs) have become widely accessible, moving AI from experiments to production. Companies now need people who can connect models to data, build services around them, and keep them fast, safe, and cost-effective. As Nvidia’s CEO