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About the Role

This role is pivotal in transforming Springer Nature's traffic, usage, and discovery data into actionable insights. You will navigate the evolving landscape of research communication, shaping how millions discover, trust, and apply research to accelerate real-world impact. The position sits at the intersection of content discoverability, content protection, and data intelligence, aiming to maximize content value across the web.

Responsibilities

Analyze 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 external signals indicating 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 behavior, identifying opportunities and risks.
Evaluate the trade-offs between maximizing 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.
Analyze WAF, traffic, and behavioral data, primarily in BigQuery, to identify scraping, bot activity, and unauthorized content extraction using fingerprint analysis, behavioral 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 prioritization and investment decisions.
Investigate incidents and anomalous traffic patterns, distinguishing legitimate institutional and authenticated users from malicious automation.
Help define the evidence base for decisions about content exposure, rate limiting, entitlement enforcement, and platform protections.
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 behavior, content consumption patterns, and external ecosystem trends.
Contribute to experimentation and measurement frameworks that assess the impact of product, metadata, discovery, and protection changes.

Requirements

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 behavioral trends.
Experience analyzing 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, behavioral analytics, fingerprinting, and content protection challenges.
Ability to connect multiple data sources to build a coherent picture of user behavior 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.

Skills

SQL
BigQuery
Data Analysis
Statistical Analysis
Web Traffic Analysis
Referral Data Analysis
User Journey Analysis
Content Discoverability
Search Ecosystems
Referral Ecosystems
Bot Detection
Behavioral Analytics
Fingerprinting
Content Protection
Data Integration
Communication Skills
Data Security
Data Governance
Python
Scripting
Dashboarding
Data Visualization
Looker
Grafana
Security Analytics
Threat Intelligence
Fraud Detection
SEO
Scholarly Discovery Services
Content Metadata
Digital Content Distribution
Access Management
Authentication Systems
Entitlement Systems
Academic Publishing
Research Information Systems
Scholarly Communications
Dataform
dbt
Classification Models
Machine Learning Models
Anomaly Detection
Segmentation

About the Company

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.