Our Digital Media division is responsible for the killer apps and digital experiences that allow you to take NPR anywhere. As an Agile-focused organization, we need to measure our success and failures quickly and credibly in order to build the best possible experiences for listeners. As the Digital Media Product Analytics intern you will have the opportunity to measure audience size, behaviors, retention, and demographics across of variety of platforms, including NPR.org, NPR One, the NPR App, enterprise products, podcasts, and more. You’ll analyze data on millions of listeners over billions of interactions under the supervision of the product team, which includes the Analytics Manager.
The intern will also have some flexibility over what you’d like to work on, with a mix of routine tasks and longer-term project work. There is so much appetite for good data at NPR, it won’t take long to become a valuable contributor to our team.
- Measure and gather insights on users, interactions, and content across a variety of NPR’s digital platforms (apps, the website, etc.)
- Partner with UX to design, prototype, and test new experiences
- Use tools like Google Analytics, New Relic, MySQL databases, App Store analytics and process data with Excel, R, Python, or the language of your choice
- Draw conclusions from the data, communicate your conclusions in write-ups, meetings, and presentations
- Build dashboards to automate data gathering and processing
- Most importantly, a quantitative mind. You’re comfortable thinking through problems logically and gathering information to answer questions.
- An interest in news and media
- Familiarity with web analytics: the concepts behind it, how to analyze the data, and experience using a tool like Google Analytics
- Communication skills. You can convince people of your conclusions and ideas in writing, speech, or graphs.
- A thirst or aptitude for programming, not necessarily experience. Being able to communicate in technical details is just as important as writing actual code. We need people who are not afraid to get their hands dirty to understand the building blocks behind a product when necessary. Bonus points for Python or R because we use that a lot in data analysis.