Senior Data Analyst II

About this role

Senior Data Analyst II
Would you like to be part of a team that delivers high-quality Analytics to our customers?
Are you a highly visible champion with a ‘can do’ attitude and enthusiasm that inspires others?

About our Team:-
Our team turns operational data into insight. By measuring and improving how content is created, validated, and delivered across Health Content Management, Data Operations, and Journals Operations, we cut turnaround times, raise quality, and give stakeholders clear visibility into performance. Every efficiency we unlock helps Elsevier deliver trusted, evidence-based content that supports providers, prepares students, improves clinical outcomes, and benefits every patient.

About the Role:-
The Senior Data Analyst II takes ownership of analysis and reporting from the first question to the final result. The work helps content move through our operations faster and more smoothly, supports better decisions based on facts, and speeds up ongoing improvement across our global teams. The role works with a high degree of independence: it turns loosely defined business questions into clear ideas to test, builds the right tools to answer them (dashboards, performance measures, and in-depth studies), and guides senior stakeholders with clear, story-driven insights. Experience in publishing is helpful but not required. What matters is a willingness to learn, or a strong existing grasp of, how content travels from start to finish and where it is handed from one team to another. This includes how content and its connected data are created and managed, where suppliers get involved, the steps in each workflow, the quality checks along the way, the points where work goes wrong or has to be redone, and the factors that drive volume and speed.
The role works closely with operations leaders and neighboring teams, such as strategy, content production, finance, the central analytics group, and technology and data teams. Together they make sure everyone measures success in the same way, relies on trustworthy data, and sees real, measurable improvements in how work gets done.

A Senior Data Analyst II is a recognized expert in their area. They increasingly influence not only what is reported, but also how the organization thinks about measurement, quality, and performance. They coach junior analysts and help business users read and use reports correctly and consistently.
Responsibilities:-

1. Stakeholder Partnership and Insight Leadership

• Lead analytics engagements independently, from clarifying objectives and framing hypotheses to defining success measures and executing end-to-end.

• Act as a thought partner to senior stakeholders: priorities meaningful metrics, offer options, trade-offs, impact sizing and monitoring plans.

• Proactively surface problem areas in the content journey (bottlenecks, defect leakage, rework, delays, supplier variability, workload imbalance) and propose targeted analyses and actions.

• Present recommendations as clear, non-technical narratives tailored to stakeholder context and decision points.
2. Reporting, Dashboards and KPI Delivery

• Deliver reports, dashboards and BI structures that meet business requirements and support trend and performance analysis.

• Define best-fit reporting: KPI definitions and levels (team to global), metric logic, segmentation, benchmarking and an adoption plan to embed usage in BAU.

• Deliver accurate, timely KPI reporting at every stakeholder level; partner with Data Management to publish trusted dashboards with consistent definitions and strong quality controls.
3. Publishing Domain and Content-Flow Process Analytics

• Apply deep knowledge of publishing and content operations to interpret workflow behavior and find improvement opportunities.

• Build measurement frameworks covering throughput and cycle time (lead, queue and touch time), stage-level performance and handoff quality, rework and defect drivers, supplier variation and risk hotspots, and productivity, capacity and workload distribution.

• Work with process owners to turn operational pain points into measurable indicators and actionable improvement levers.
4. Data Preparation, Integration and Engineering Literacy

• Handle large, complex datasets and combine multiple sources using SQL and modern analytics tooling.

• Understand integration mechanics: pipelines, transformations, joins, grain, lineage, refresh cadence and performance constraints.

• Work with big data platforms (Databricks preferred); SQL essential; Python and/or Scala desirable; KNIME, Tableau/Power BI and Snowflake exposure an advantage.

• Bridge business needs and technical implementation so solutions are scalable, maintainable and aligned to platform standards.
5. Advanced Analytics, Modelling and Statistical Thinking

• Apply techniques suited to the problem: trend decomposition, segmentation, cohort analysis, anomaly detection, hypothesis testing, correlation exploration and scenario modelling.

• Develop predictive analytics, basic machine learning and NLP use cases where relevant, and translate outputs into operational recommendations, guardrails and monitoring metrics, not just model accuracy.
6. Data Governance, Quality and Metric Consistency

• Safeguard quality, integrity, completeness, timeliness and reliability; run regular data quality assessments and resolve gaps and discrepancies.

• Align metric definitions across teams to remove inconsistencies between dashboards; lead or co-lead documentation (definitions, KPI logic, change logs, refresh schedules, known limitations).

• Champion a data quality culture in which data trust is foundational to performance management.
7. Coaching, Enablement and Community Leadership

• Coach junior analysts on data preparation, modelling, visualisation standards, stakeholder management and project execution.

• Train and support reporting champions and business users for consistent, effective use of reports; promote problem resolution and…