System Data Scientist
Teknik, data och digitalt · Data, AI och analys · Datavetenskap · Maskininlärning · Artificiell intelligens
I korthet
Ericsson is seeking a System Data Scientist in Stockholm to lead their analytics function, focusing on software development, research, and AI/ML initiatives. This role involves building data systems and analytical frameworks, leveraging AI/ML expertise (LLMs, GenAI, traditional ML) and full-stack data science capabilities with end-to-end system ownership.
Ansvarsområden
- Establish and lead Ericsson's analytics function across software development, research, and AI/ML initiatives.
- Build comprehensive data systems and analytical frameworks for data-driven decision-making.
- Combine AI/ML expertise (LLMs, GenAI, traditional ML) with full-stack data science capabilities and end-to-end system ownership.
- Define system requirements, optimize data pipelines, and ensure analytical infrastructure supports organizational needs.
- Transform raw information into trusted data for artificial intelligence and Agentic workflows.
- Drive AI/ML initiatives including GenAI, LLMs, and traditional machine learning across diverse use cases.
Krav
- Master’s degree or PhD in Telecommunication or similar.
- Experience in driving studies in telecommunications or AI technical areas.
- Experience in presenting technical information to larger audiences.
- Python programming for production environments.
- Expertise in managing large data sets and data pipelines.
- Experience with process in systematization of products.
- A productive, creative mindset to interact easily with other people.
Förmåner
- Outstanding opportunity to use your skills and imagination to push boundaries.
- Build solutions never seen before to some of the world’s toughest problems.
- Be challenged, but not alone; join a team of diverse innovators.
- Work towards crafting what comes next.
- Encouraging a diverse and inclusive organization is core to our values.
#Data Science#AI/ML#LLMs#GenAI#Machine Learning#System Ownership#Python#Data Pipelines#Analytics