Cisco ThousandEyes · Bangalore, India
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Meet the Team-SiOps Component Central Operations (CCO) supports Cisco’s Components (Silicon and Optical) supply chains, driving business transformation and enabling scalable, profitable growth. The CCO Data & Analytics team brings together expertise in business architecture, advanced data science, AI, analytics, and program management to solve complex business challenges and enable strategic outcomes. We work in a fast-paced, globally connected environment, partnering across business and technology teams to turn data, AI, and analytics into meaningful business impact. Your Impact As a Data Scientist within the CCO Data & Analytics team, you will design and develop high-impact AI/ML solutions that transform complex and ambiguous component supply chain challenges into measurable business outcomes. You will combine advanced statistical and machine learning expertise with emerging Generative AI, LLM, and Agentic AI capabilities to develop scalable solutions that improve supply chain decision-making, operational efficiency, and business performance. You will work closely with AI Engineers, business stakeholders, and technology teams to take solutions from research and experimentation through production deployment and continuous improvement. You Will Translate complex business objectives into scalable and rigorous data science and AI/ML solutions. Develop and deploy advanced AI/ML models, including predictive analytics, LLM-powered applications, and Agentic AI workflows. Apply advanced statistical methods, experimental design, model evaluation, and statistical validation to ensure methodological rigor. Build solutions using supervised and unsupervised learning, time-series analysis, optimization, and statistical modeling. Design and develop RAG pipelines, including embeddings, retrieval strategies, reranking, context management, and evaluation. Develop and optimize LLM-powered applications, including prompt engineering, structured outputs, guardrails, and workflow optimization. Explore emerging AI methodologies such as Agentic AI, multi-agent orchestration, tool calling, graph analytics, and LLM fine-tuning. Partner with AI Engineers and technology teams to transition data science solutions from experimentation into production. Design end-to-end data pipelines and AI solutions that are scalable, reliable, and production-ready. Work with large-scale structured, unstructured, and multi-modal datasets to generate actionable insights. Apply MLOps practices to support model deployment, monitoring, lifecycle management, and continuous improvement. Drive innovation by evaluating emerging AI/ML technologies and identifying opportunities to apply them to supply chain challenges. Ensure solutions follow enterprise standards for responsible AI, ethics, bias mitigation, interpretability, and reproducibility. Communicate complex analytical findings clearly and translate them into actionable recommendations for business and non-technical stakeholders.
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