Sr Data Scientist

Job highlights

• 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.

Job Summary

Ericsson Enterprise Wireless Solutions (BEWS) drives Ericssons 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.

Responsibilities

• 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.

Requirements

• 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.

Qualifications

• Masters 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.

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