The way that academic research is being communicated to audiences around the globe is changing rapidly. Increasingly, research data can be consumed at scale through AI tools and machine intelligence, creating a moment of transition in research communication, and an opportunity to shape how millions discover, trust, and apply research, accelerating the translation of insight into real‑world impact faster than before.
The purpose of this role is to turn Springer Nature’s traffic, usage and discovery data into the signals and evidence the team relies on to maximise the value of our content across the web, navigating this transition. This role sits at the intersection of content discoverability, content protection and data intelligence.
You will help the organisation understand how Springer Nature content is being discovered, accessed and used across search engines, academic discovery services, AI-powered tools, content aggregation platforms and other external channels. At the same time, you will identify and measure inappropriate access, automated scraping and content extraction activities to ensure discoverability is achieved without undermining platform traffic, entitlements or commercial value.
You will own the analytical foundations of both disciplines: identifying and measuring the signals that indicate successful discovery, as well as those that indicate abuse. Working closely with product, platform, analytics and security teams, you will provide the evidence needed to shape strategy, prioritise interventions and measure outcomes.