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We’re hiring on behalf of a global company in the retail and fashion industry. They’re looking for a mid-level Data Scientist to join their growing data science team and help solve real-world challenges in areas such as marketing personalization, supply chain optimization, product strategy, and more.
If you’re a technically skilled analyst with a passion for machine learning and a desire to grow in a product-driven, consumer-focused environment — this role is for you.
2–4 years of hands-on experience in a data science or machine learning role.
Degree in Computer Science, Mathematics, Statistics, Engineering, or related field.
Proficiency in Python and libraries such as pandas, scikit-learn, NumPy (R also welcome).
Solid foundation in statistical modeling and machine learning (regression, classification, clustering, time series).
Comfortable working with SQL and large datasets.
Familiarity with cloud platforms (GCP preferred; AWS or Azure also welcome).
Ability to explain technical results to business audiences.
A curious mindset, strong attention to detail, and a desire to learn and grow.
Experience in the retail or consumer goods industry is a plus.
Python (core language)
SQL (for analysis and extraction)
Google Cloud Platform (GCP) – preferred cloud environment
Employment agency entry number 47
this job offer is intended for people over 18 years of age
Full-time employment contract
Fully remote work – collaborate from anywhere in Poland
Attractive benefits package, including private medical care, sport card, and personal development support
Work with a forward-thinking global brand on high-impact data products
Exposure to cross-functional projects in eCommerce, marketing, product, and supply chain
Supportive, knowledge-sharing data science team environment
Design, build, and deploy predictive models and machine learning solutions to support business decisions.
Conduct deep-dive data analyses to uncover actionable insights.
Work cross-functionally with data scientists, engineers, and business stakeholders to solve end-to-end problems.
Participate in all phases of the data science project lifecycle: ideation, exploration, modeling, deployment, and monitoring.
Translate technical findings into clear and concise business recommendations.
Contribute to A/B testing efforts and impact measurement initiatives.
Keep documentation of models and code organized, reproducible, and accessible.
Actively stay up to date with new data science methodologies and tools.
Support ongoing improvements in data science workflows and collaboration.