Telecom Data Scientist | Early Career Program

About this opportunity

What you will do

You will join a dynamic and international environment where you’ll have the opportunity to learn, grow and make a real business impact. As part of the Applied Generative AI team, you will use data science, machine learning and generative AI to help transform Ericsson R&D from a development flow organized in separate disciplines into a connected, data-driven and AI-native way of working. You will:

• Translate Radio Access Network (RAN) and product-development challenges into data science and AI problems, define success measures, and evaluate results with engineering and domain teams.
• Make engineering information, including specifications, architecture, code, tests, logs and operational reports, useful and trustworthy for AI.
• Build data pipelines, knowledge systems and retrieval capabilities that connect engineering information in different formats.
• Experiment with and evaluate machine learning and generative AI, including large language models and AI-agent systems.
• Connect code, system knowledge and operational evidence to support work throughout development, verification, deployment, operation and troubleshooting.
• Help develop and assess AI models for Ericsson technologies and terminology with RAN specialists and users.
• Turn successful experiments into maintainable engineering capabilities, with attention to data quality, security, responsible AI, monitoring and human review.

You will bring
You are curious and pragmatic, take initiative, ask questions and work well across disciplines. You combine a strong foundation in data science, machine learning and generative AI with knowledge of telecommunications or radio networks, and want to apply it to real product-development challenges. Experience in software or product development is an advantage.

In addition, you have:

• MSc or PhD in data science, computer science, AI, machine learning, telecommunications, electrical or software engineering, mathematics, or a related field.
• Applied data science and machine-learning skills, including statistics, experimentation, model development and evaluation.
• Practical generative-AI experience in one or more areas such as large language models, retrieval-augmented generation (RAG), model adaptation, AI agents or model evaluation.
• Knowledge of wireless communications or radio networks, and interest in building deeper RAN expertise.
• Hands-on programming and software-development experience, including work with complex data and reproducible analyses, models or pipelines.
• An understanding of how product development connects requirements, architecture, implementation, verification, release and maintenance.
• Analytical judgement, an experimental mindset, and the ability to explain uncertainty, model limitations and technical trade-offs.
• Strong communication and collaboration skills, proficiency in English, and readiness to work in global, cross-functional teams.