About this opportunity:
The RAN System Management & Architecture sector leads and enables a distributed systemization and engineering of the constituent parts of the RAN system. We are seeking for an experienced System Data Scientist to establish and lead our RAN data platform across system development, research, AI/ML and agentic workflow initiatives.
In this role, you will build comprehensive data systems and analytical frameworks that enable data-driven decision-making across the organization. You will combine advanced AI/ML expertise (LLMs, GenAI, traditional ML) with full-stack data science capabilities and end-to-end system ownership. Leading a team of data scientists, you will design scalable data architectures, implement analytical systems, and deliver high-impact insights. Working closely with the Senior Data Platform Architect, you will define system requirements, optimize data pipelines, and ensure analytical infrastructure supports organizational needs.
What you will do:
- Lead AI/ML initiatives including GenAI, LLMs, and traditional machine learning across diverse use cases and business scenarios
- Develop and deploy production ML models with expertise in model versioning, monitoring, and A/B testing (MLOps practices)
- Build and implement GenAI applications and LLM including prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG) solutions
- Extract actionable insights from complex data and drive data-driven decisions across CI/CD optimization, research experimentation, product metrics, and business operations
- Perform advanced feature engineering and model optimization to ensure high-performance analytical solutions
- Build time series forecasting models for CI/CD predictions and capacity planning
- Collaborate with data platform and engineering teams to define requirements and implement solutions
- Lead and mentor a team of 4-6 data analysts, fostering innovation and continuous improvement
The skills you bring:
- 3+ ML models deployed to production with hands-on experience in LLMs (OpenAI, Anthropic, open-source) and GenAI applications (chatbots, code assistants)
- Advanced Python programming for production environments and expertise in snowflake or Apache Spark for large-scale data processing
- SQL mastery including complex queries and optimization; experience with data lakehouse technologies (Iceberg, Delta Lake, Parquet)
- Understanding of distributed systems, production data pipelines, and MLOps practices
- Advanced statistical methods (hypothesis testing, regression, causal inference) and ability to translate business questions into analytical solutions
- Can articulate model architecture choices and technical tradeoffs
- Nice-to-have: Kubernetes/Docker, Apache Airflow, streaming data processing (Kafka, Spark Streaming), dbt, and software development metrics dashboards
- Master's or PhD in Data Science, Statistics, Computer Science, or related field
- Optional certifications: Google Professional ML Engineer, AWS ML Specialty, Databricks ML Professional
Location
Kista, Stockholm (Sweden), Athlone (Ireland), work from one of our Ericsson offices in these locations.
