Senior Data Scientist

Join our Team

About the Opportunity:

Ericsson Enterprise Wireless Solutions (BEWS) drives Ericsson's Enterprise Networking and Security business. Our growing portfolio spans WAN, LAN, and enterprise security solutions. We are the global leader in Wireless-WAN enterprise connectivity and are rapidly expanding our Private 5G and Secure Access Service Edge (SASE) offerings.

What Will You Do:

Lead the design and deployment of machine learning and statistical models that solve complex business challenges. Architect and build Agentic AI solutions including multi-agent systems, autonomous reasoning loops, tool-using agents, and orchestration frameworks for intelligent network operations. Design and implement LLM-powered applications using RAG, domain-adapted models, prompt engineering, structured outputs, and evaluation frameworks such as RAGAS and LLM-as-Judge. Partner with data engineers, product managers, and architects to identify high-value AI opportunities and deliver scalable production solutions. Perform data exploration, feature engineering, and dataset preparation for ML and LLM-based systems. Evaluate model performance and ensure robustness, interpretability, fairness, and reliability across traditional ML and transformer-based models. Develop reusable AI frameworks, tools, and libraries that improve team productivity and solution quality. Stay current with advancements in AI/ML research, open-source technologies, and industry trends.

What You Will Bring:

8–14 years of experience in Data Science and Machine Learning with increasing ownership and technical leadership. Strong expertise in regression, classification, clustering, ensemble methods, time-series analysis, and anomaly detection. Hands-on experience building LLM solutions using RAG, prompt engineering, vector databases, context management, and orchestration frameworks. Experience designing Agentic AI workflows, including tool-calling agents, memory architectures, and autonomous decision-making systems. Knowledge of model fine-tuning techniques including LoRA, QLoRA, PEFT, instruction tuning, and RLHF/DPO approaches. Strong programming skills in Python, proficiency in SQL, and familiarity with distributed data processing platforms. Solid software engineering practices with clean, testable, reproducible code and CI/CD awareness. Experience deploying, monitoring, and scaling AI models in production environments. Proven track record of leading AI/ML initiatives from problem definition through deployment and continuous improvement.

Preferred Qualifications:

Master's or PhD in Computer Science, Statistics, Mathematics, or a related discipline. Experience with deep learning frameworks such as PyTorch and TensorFlow, including training or fine-tuning large models. Understanding of responsible AI practices, model safety, guardrails, hallucination mitigation, bias evaluation, and output filtering. Experience with function-calling and tool-use patterns in LLM applications and reliable agent design. Exposure to telecom, network operations, AIOps, or related domains is highly desirable. Contributions to open-source projects or published research are an advantage.

All academic credentials must be from recognized and accredited institutions and are subject to verification.