Are you up for a challenge? Then step up to the plate – this role is for you!

As a Senior Data Scientist, you’ll be responsible for the full lifecycle of machine learning models — from exploring a problem and building the first prototype to deploying models to production and continuously monitoring their performance. You’ll work closely with Software Engineers, Product Managers and Designers to turn complex data into practical, production-ready solutions.

✅ If your experience and skills match our criteria for the role, apply for this job now. We will love to meet you!
*Find out more about our recruitment process here.

Location:

Cracow

Employment type:

B2B, 130-160 pln/h

Start:

Asap

Hours:

CET working hours

Mode:

remote/hybrid/office

Recruitment process:

❇️ Intro chat with Basia - let’s sync up and get to know each other!
❇️ Client meeting - an open conversation about your background
❇️ Final alignment - team feedback and next steps

In this role you will

➡️ Lead the end-to-end development and delivery of machine learning models — from discovery and feature engineering to production, A/B testing and monitoring.
➡️ Work closely with Product and Engineering teams to turn complex data into solutions supporting payment and payer fraud prevention.
➡️ Research and apply the latest developments in machine learning and fraud prevention.
➡️ Help shape the technical direction and solutions used within the Payment Intelligence team.
➡️ Share knowledge, contribute to code reviews and help raise engineering and data science best practices across the team.
➡️ Work on models and solutions designed to operate at scale within a production environment.

Must have

➡️ Degree or PhD in a STEM discipline, or equivalent commercial experience.
➡️ Hands-on experience with advanced architectures, such as deep learning, graph-based or sequence-based models.
➡️ Experience in FinTech, Fraud Prevention or Payments is a strong plus.
➡️ Ability to translate complex ML concepts into practical product solutions and explain them to non-technical stakeholders.
➡️ Experience with the full ML lifecycle — from analysis and feature engineering to prototyping, validation and A/B testing.
➡️ Strong coding skills and a focus on clean, high-quality code.
➡️ Experience sharing knowledge and contributing to technical best practices.