Senior Data Analyst

Overview

In this role you will act as a trusted analytics partner, leading complex data projects from planning to delivery. You will translate business questions into actionable data-driven plans and influence senior stakeholders with insights. You’ll work autonomously, mentor junior analysts, and collaborate with cross-functional teams to deliver impact in the STMJ data and analytics space. The opportunity sits at the heart of Elsevier’s mission to advance science and improve health outcomes through data-driven decision making. You will shape strategy and drive performance by applying evolving analytics techniques in a global context.

Pay / Benefits

• Generous holiday allowance
• Health and wellbeing benefits
• Pension and savings plans
• Life assurance and income protection
• Flexible and hybrid working options
• Learning and development opportunities

Responsibilities

• Lead analytics projects from planning through delivery, turning vague problems into concrete actions
• Partner with senior stakeholders to shape strategy using hypothesis-driven thinking and challenge assumptions with data
• Identify and implement the most relevant analyses, metrics, and reporting solutions
• Manage multiple projects concurrently with effective prioritisation
• Collaborate with cross-functional teams to overcome challenges and deliver results
• Evaluate and adopt new tools and techniques, including AI-assisted analytics
• Coach and mentor junior data analysts and share best practices

Key requirements

• Proven ability to influence decision-making with data-backed recommendations
• Experience managing projects and delivering results independently
• Solid grounding in analytics concepts (data modelling, blending, visualization)
• Ability to balance multiple priorities in a fast-paced environment
• Strong problem-solving and critical-thinking skills
• Comfort with a dynamic technical landscape and learning new tools and methodologies
• Experience in scholarly communications, publishing, or related field is advantageous but not required

• stakeholder influencing
• collaboration across functions
• coaching and mentorship
• data modelling
• data blending
• data visualization

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