Internet Measurement Researcher PhD (Intern) - United States

Please note this posting is to advertise potential job opportunities. This exact role may not be open today but could open in the near future. When you apply, a Cisco representative may contact you directly if a relevant position opens. Applications are accepted until further notice. This role may be eligible for remote work in the United States depending on business needs and hiring manager approval. Meet the Team We’re the Applied Research team at Cisco ThousandEyes. Our mission is simple but ambitious: use unmatched Internet-scale data to build network assurance solutions that help enterprises see, understand, and fix problems across the Internet, cloud, and enterprise networks. We sit at the intersection of research, data science, AI/ML, and product. That means we don’t just analyze data. We turn ideas into algorithms, prototypes into products, and insights into features that customers rely on when things break at 3am! Your Impact This internship sits within the Endpoint research group, where we build solutions that give enterprises deep insight into how SaaS, collaboration, and internal applications perform for a hybrid workforce; at home, in the office, or on the go. The internship will focus on device health and telemetry data collected from mobile devices. As an intern, you’ll work with real-world network data at enterprise scale and tackle concrete, open-ended research problems. You’ll use statistics, data science, and AI/ML techniques to better understand how we can leverage data gathered from mobile devices (e.g., end-to-end network tests, cellular signal metrics) to inform our event detection algorithms. Throughout the internship, you’ll collaborate closely with experienced researchers, engineers, and product partners. You’ll be mentored by a dedicated researcher and own a well-scoped project, allowing you to see your work progress from exploration and prototyping toward solutions that can influence real products used by large enterprises. This internship is designed to be hands-on, impactful, and fast-moving, with a strong emphasis on learning, experimentation, and turning research ideas into practical solutions. In addition to influencing production systems, your work may contribute to internal research artifacts, technical reports, or peer-reviewed publications. Minimum Qualifications Currently enrolled in a PhD program in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.  Strong understanding of networking and internet protocols (TCP/IP, DNS, BGP, HTTP, etc.) Existing portfolio of network measurement projects (publication and/or course projects) Great written, verbal, and persuasive communication skill Proficiency in Python and common data science libraries (pandas, NumPy, scikit-learn, etc) Experience querying and working with data in SQL and NoSQL systems at scale Preferred Qualifications Background in cellular networks and experience on mobile systems is a plus 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. Message to applicants applying to work in the U.S. and/or Canada: Individual pay is determined by the candidate's hiring location, market conditions, job-related skills ... (truncated, view full listing at source)