Data Scientist - Automation & Innovation Department @ T-Mobile Polska
(práce v Polsku)

Data Scientist - Automation & Innovation Department @ T-Mobile Polska

Polsko

• Minimum 4 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.
• 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).

We are looking for a Data Scientist with a proven experience and a passion for turning complex telecommunications data into actionable insights.

This role combines deep analytical skills with hands-on technical execution and requires experience in deploying scalable machine learning solutions in real-world environments.

,[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] Requirements: Machine learning, Python, scikit-learn, TensorFlow, PyTorch, Big data, Spark, Hadoop, Data analytics, SQL, Relational database, Git, Jira, Confluence, Cloud, AWS, GCP, ETL, Communication skills

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