Senior Data Scientist / Senior Machine Learning Engineer

Quick Facts

• Role: Senior Data Scientist / Senior Machine Learning Engineer

Description

You will design, build, and develop end-to-end ML/AI solutions supporting personalization, recommendations, automation, and product decision-making. The role spans the full ML lifecycle—from data analysis and experimentation to model validation, production deployment, monitoring, and continuous improvement. You will also implement solutions for near real-time or real-time scoring and recommendations based on current user signals.

Responsibilities

• Design, create, and evolve ML/AI models and solutions for personalization, recommendations, automation, and product decisions.
• Lead the complete ML lifecycle: data analysis and experiments, model building and validation, deployment, monitoring, and further development.
• Design end-to-end solutions covering data, architecture, integration with other systems, and how results are used by product and business.
• Deploy models to production on Google Cloud using BigQuery and Gemini Enterprise Agent Platform / Vertex AI tooling.
• Build and automate ML pipelines for training, scoring, deployment, and monitoring.
• Develop features running in near real-time or real-time, e.g., scoring, recommendations, personalization, and decisioning based on live user signals.
• Collaborate with Product, CRM, Analytics, and Data Engineering teams on ML solution design and rollout.

Requirements

• Several years of practical experience building, deploying, and evolving Data Science / Machine Learning solutions.
• Excellent knowledge of Python and SQL, including work with large datasets.
• Experience building, validating, and deploying ML models in production environments.
• Ability to look at solutions end-to-end: data and model, architecture and integration, and business impact.
• Practical knowledge of ML libraries/frameworks such as pandas, NumPy, scikit-learn, XGBoost / LightGBM, TensorFlow, or PyTorch.
• Experience with Google Cloud, especially BigQuery and ML/MLOps services from Vertex AI / Gemini Enterprise Agent Platform.
• MLOps knowledge: pipelines, model registry, deployment, monitoring, and CI/CD.
• Independence, ownership, and strong communication with both technical and business stakeholders.

Benefits

• Dedicated onboarding support from a partner.
• Modern office in Katowice.
• Free English classes.
• MedicoverSport card, life insurance, and medical care.
• Free tickets to Polish national team matches.
• Affordable, varied breakfasts and lunches.
• Personal development track and budget for training and courses.
• Daily fresh fruit, good coffee, and healthy snacks.
• Respect for work-life balance.