Data Scientist - Automation & Innovation Department

T-Mobile Polska is seeking a Data Scientist to work with telecommunications data, turning complex data into actionable insights and deploying scalable machine learning solutions in real-world environments.

• Minimum 3 years of proven industry experience in data science or applied machine learning.
• Strong programming skills in Python.
• Expert knowledge of supervised and unsupervised machine learning methods.
• Hands-on experience with ML libraries and frameworks such as Scikit-Learn, TensorFlow, PyTorch.
• Proficient with big data tools (e.g., Spark, Hadoop) and orchestration tools.
• Solid foundation in data analytics, data visualization, data mining, statistics, and probability theory.
• Experience with SQL and relational databases.
• Familiar with agile methodologies and software development practices (Git, JIRA, Confluence).
• Experience working in cloud environments (Data Robot, AWS and GCP are a plus).
• Understanding of big data ecosystems and ETL processes.
• Nice to have - previous experience working with telecom datasets (e.g., network KPIs, customer behavior).
• Strong problem-solving skills and a proactive, detail-oriented mindset.
• Excellent communication skills with the ability to present findings to diverse audiences.
• Fluent in English (written and spoken).

• Extract and analyze network and business data to identify trends, detect anomalies, and recommend improvements in service performance and customer experience.
• Apply advanced analytical techniques and develop predictive models and algorithms that support telecom operations across the value chain.
• Develop, test, and deploy data models into production, supporting automation and performance monitoring.
• Collaborate with cross-functional teams including engineering, product, and operations to define analytical goals and deliver impactful results.
• Maintain and enhance the model development environment including pipelines and orchestration frameworks.
• Visualize and explain the outcomes of the work in a way that is understandable for diverse audiences.