Purpose of the role
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.
Key Responsibilities
Content Discoverability & External Platform Analytics
- Analyse how Springer Nature content is discovered across external channels, including search engines, scholarly discovery services, library platforms, aggregators, citation networks, AI-powered discovery tools and emerging content platforms
- Develop and maintain a framework of discoverability metrics, signals and KPIs to measure the effectiveness of content distribution and discovery strategies
- Identify the external signals that indicate successful content discovery, engagement and conversion back to Springer Nature properties
- Measure the impact of metadata quality, indexing, platform integrations and content syndication on discoverability outcomes
- Track changes in referral patterns, search visibility and external platform behaviour, identifying opportunities and risks
- Evaluate the trade-offs between maximising reach and preserving platform traffic, user engagement and commercial value
- Provide recommendations to product and business stakeholders on how content should be surfaced, exposed and protected across external ecosystems
- Build analytical models to understand the relationship between discoverability, content consumption, platform traffic and downstream business outcomes
Content Protection & Traffic Intelligence
- Analyse WAF, traffic and behavioural data, primarily in BigQuery, to identify scraping, bot activity and unauthorised content extraction using fingerprint analysis, behavioural signals and network data
- Build and maintain a portfolio of detection signals and continuously evolve them as threat actors change their tactics
- Measure detection performance through coverage, precision, false-positive rates and baseline benchmarking
- Quantify the scale and commercial impact of content scraping and content leakage to support prioritisation and investment decisions
- Investigate incidents and anomalous traffic patterns, distinguishing legitimate institutional and authenticated users from malicious automation
- Work closely with the Security Specialist Engineer, who will implement and enforce controls, while you identify, measure and validate the underlying signals
- Help define the evidence base for decisions about content exposure, rate limiting, entitlement enforcement and platform protections
Insight, Reporting & Stakeholder Engagement
- Design and maintain dashboards and reporting that communicate discoverability performance, content protection effectiveness and emerging risks
- Translate complex data findings into clear recommendations for product, platform, security and senior stakeholders
- Establish meaningful baselines, benchmarks and success measures for both discoverability and protection initiatives
- Support product strategy by providing evidence-driven insights into user behaviour, content consumption patterns and external ecosystem trends
- Contribute to experimentation and measurement frameworks that assess the impact of product, metadata, discovery and protection changes
Essential Skills & Experience
- Strong SQL skills, particularly BigQuery, with experience working on large-scale datasets.
- Strong analytical and statistical capability, including the ability to identify patterns, anomalies and behavioural trends
- Experience analysing web traffic, referral data and user journeys across digital products.
- Understanding of content discoverability, search and referral ecosystems, or the ability to quickly develop expertise in this area
- Interest in bot detection, behavioural analytics, fingerprinting and content protection challenges
- Ability to connect multiple data sources to build a coherent picture of user behaviour and content usage
- Excellent communication skills, with the ability to turn data insights into actionable recommendations and compelling stakeholder narratives
- Strong understanding of data security and governance
Desirable
- Python or other scripting experience for data analysis and automation
- Experience with dashboarding and visualisation platforms such as Looker, Grafana or similar tools
- Exposure to security analytics, threat intelligence or fraud detection
- Understanding of SEO, scholarly discovery services, content metadata or digital content distribution ecosystems
- Familiarity with access management, authentication or entitlement systems
- Experience within academic publishing, research information systems or scholarly communications
- Experience with modern data transformation tooling such as Dataform or dbt
- Experience developing classification or machine learning models for user behaviour analysis, anomaly detection or segmentation
- Experience measuring the impact of external platforms on traffic, engagement, discoverability and commercial outcomes
