Cisco—Data Scientist

Job description

Description: Meet the Team 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. - Mentor team members and contribute to a culture of technical excellence, collaboration, and continuous learning. Who You’ll Work With You will collaborate closely with CCO business teams, Supply Chain, AI Engineering, Data Engineering, IT, Product and Technology teams, and global cross-functional stakeholders to develop and deploy AI-powered solutions that deliver measurable business outcomes. Minimum Qualifications - Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative discipline. - 7–10+ years of professional experience in Data Science, Advanced Analytics, Machine Learning, or a related field. - Strong experience applying statistical analysis, correlation analysis, and advanced statistical techniques to solve business problems. - Expert-level proficiency in Python and SQL. - Strong understanding of supervised and unsupervised machine learning, time-series analysis, and optimization techniques. - Hands-on experience developing and deploying AI/ML models and LLM-powered applications in production environments. - Experience with Agentic AI, autonomous workflows, tool calling, or multi-agent orchestration. - Experience building RAG pipelines, semantic retrieval systems, embeddings, vector databases, and AI evaluation frameworks. - Strong prompt engineering skills, including prompt design, structured outputs, guardrails, and workflow optimization. - Experience designing end-to-end data pipelines that support production AI/ML systems. - Experience with MLOps and model lifecycle management. - Proven ability to translate complex analytical findings into actionable business insights. - Strong problem-solving, communication, collaboration, and stakeholder-management skills. Preferred Qualifications - Experience applying AI/ML and advanced analytics to supply chain, manufacturing, semiconductor, or operational environments. - Hands-on experience with MCP (Model Context Protocol), A2A (Agent-to-Agent) communication patterns, and agent integration frameworks. - Experience with LLM fine-tuning, human-in-the-loop validation, and AI evaluation frameworks. - Experience with modern data platforms such as Snowflake or equivalent technologies. - Experience working with large-scale unstructured and multi-modal datasets. - Proven track record of deploying AI/ML models that have directly improved supply chain performance, operational efficiency, forecasting, planning, or decision-making. - Experience with graph analytics, optimization, predictive modeling, and advanced forecasting. - Strong understanding of responsible AI, model governance, explainability, and bias mitigation. - Experience working in global, cross-functional organizations and fast-paced technology environments. Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you. Disclaimer To ensure that we hire the best talent in the right way, we follow a strict hiring process and recently, Cisco has been made aware of fraudulent recruiters claiming to be from the company. Please be advised that any communication from Cisco about careers will: - be in direct response to an application you have submitted through the company career site - begin with screening or an interview - originate from a Cisco email address, and - be conducted across email, phone, or WebEx Cisco will never make a job offer without conducting an interview process or ask you for money in any way. If you have been requested to apply for a role or have received an offer from a site other than https://careers.cisco.com or cisco.wd5.myworkday.com, do not provide any personal identifying information, including your Aadhaar or other personal identifying number, birth certificate, banking information, driver's license, or passport. If you are the target of a recruiting scam, consider filing a report with your local law enforcement authorities. Cisco bears no responsibility, and cannot be held liable, for any claims, damages, expenses, or other inconvenience resulting from or in any way connected to recruiting scams. Skills: python, sql, machine learning, statistical analysis, predictive analytics, time series analysis, optimization, rag pipelines, prompt engineering, model deployment, mlops, data pipeline design, llm-powered applications, agentic ai, tool calling, multi-agent orchestration, graph analytics, embeddings, vector databases, ai evaluation frameworks, snowflake, responsible ai, model governance, bias mitigation, interpretability, stakeholder management, cross-functional collaboration, mcp, a2a, agent integration