Senior Data Analyst II

Sr Data Analyst II (Content Flow Analytics)

Would you like to be part of a team that delivers high-quality software to our customers?

Are you a highly visible champion with a ‘can do’ attitude and enthusiasm that inspires others?

About our Team:-

Elsevier Health applies innovation, facilitates insights, and helps drive more informed decision-making for our customers across global health. We support health providers by providing accessible, trusted evidence-based information; prepare more medical and nursing students with effective tools and resources; provide insights that help clinicians improve patient outcomes; and supports a more personalized and localized healthcare experience. All for the benefit of every patient.

About the Role: -

The Senior Data Analyst II leads the end-to-end delivery of analytics and reporting that improve content-flow performance , strengthen data-driven decision making , and accelerate continuous improvement across global Operations. This role operates with a high degree of independence—translating ambiguous business questions into structured hypotheses, building the right data products (dashboards, KPI frameworks, deep-dive analysis), and influencing senior stakeholders through clear, story-led insights.

Publishing domain exposure is preferred (not mandatory); the role is expected to bring/build a strong understanding of the content journey and operational handoffs, including linked data (CP/LD). (e.g., Content production/management interfaces, supplier touchpoints, workflow steps, quality gates, exceptions/rework loops, throughput and cycle time drivers). The role partners closely with Operations leadership and adjacent teams (e.g., Strategy, Content production operations teams, Finance, Analytics CoE, IT/ Data Warehousing teams) to ensure consistent metrics, trusted data foundations, and measurable operational outcomes.

A Senior Data Analyst II is a recognised analytics expert within their domain, increasingly shaping not just outputs but also how the organisation thinks about metrics, quality, and performance management . They coach junior analysts and enable business users to adopt reporting correctly and consistently.

Responsibilities:

1) Stakeholder Partnership and Insight Leadership

Lead analytics engagements fully independently: clarify objectives, frame hypotheses, define success measures, and drive execution end-to-end.

Act as a thought partner to senior stakeholders—guiding them through hypothesis thinking, prioritising the most meaningful metrics, and shaping decisions using evidence.

Proactively identify problem areas in the content journey (bottlenecks, defect leakage, rework, delay drivers, supplier variability, workload imbalance) and propose targeted analyses and corrective actions.

Present insights and recommendations in compelling, non-technical narratives—tailored to stakeholder context, decision points, and operational realities.

Support senior leaders through decision cycles by offering options, trade-offs, impact sizing, and monitoring plans.

2) Reporting, Dashboards, and KPI Delivery

Ensure delivery of reports/dashboards/BI structures that meet business requirements and support ongoing trend and performance analysis.

Define “best-fit” reporting solutions (what to build, why it matters, and how it will be used), including:

KPI definitions and levels (team/manager/function/global views)

metric logic, filters, segmentation, and benchmarking approach

adoption plan to embed usage into BAU operations

Deliver accurate and timely KPI reporting across all relevant stakeholder levels, enabling clear identification of performance focus areas.

Partner with Data Management to publish trusted dashboards to Operations users and senior governance forums with consistent definitions and strong data quality controls.

3) Publishing Domain and Content-Flow Process Analytics

Apply deep understanding of publishing/content operations to interpret workflow behaviour and identify improvement opportunities.

Build and maintain measurement frameworks for content flow, such as:

throughput and cycle time analysis (lead time, queue time, touch time)

stage-level performance and handoff quality

exception/rework drivers, defect trend analysis, root cause themes

supplier performance variation and operational risk hotspots

productivity, capacity, and workload distribution insights

Collaborate with process owners to convert operational pain points into measurable indicators and actionable improvement levers.

4) Data Preparation, Integration, and Engineering Literacy

Handle large and complex datasets; combine data from multiple systems and sources using SQL and modern analytics tooling.

Demonstrate strong understanding of data integration mechanics and data engineering concepts (pipelines, transformations, joins, grain, lineage, refresh cadence, performance constraints).

Work effectively with big data platforms and tools (Databricks preferred) and demonstrate fluency in:

SQL (essential)

Python (desirable) and/or Scala (desirable, not essential)

additional tooling such as / similar to KNIME, Tableau/Power BI, and warehouse technologies (Snowflake exposure is an advantage)

Act as a bridge between business needs and technical implementation—ensuring solutions are scalable, maintainable, and aligned to platform standards.

5) Advanced Analytics, Modelling, and Statistical Thinking

Undertake complex analyses using statistical and analytical techniques appropriate to the problem, including:

trend decomposition, segmentation, and cohort analysis

outlier/anomaly detection and exception monitoring

hypothesis testing, correlation/association exploration, scenario modelling

Develop (or continue developing) advanced analytics capabilities such as predictive analytics, basic machine learning applications, and NLP use cases where relevant to content operations.

Translate modelling outputs into operational recommendations, guardrails, and monitoring metrics (not just model accuracy).

6) Data Governance, Quality, and Metric Consistency

Ensure governance for quality, integrity, completeness, timeliness, and reliability so reporting is trustworthy.

Carry out regular data quality assessments in new and existing reporting solutions; identify and resolve gaps and discrepancies.

Coordinate with cross-functional teams to align on metric definitions and eliminate inconsistencies across dashboards and stakeholder reporting.

Lead (or co-lead) documentation practices: data definitions, KPI logic, reporting change logs, refresh schedules, and known limitations.

Act as an influencer for data quality culture—motivating stakeholders to treat data trust as foundational to performance management.

7) Coaching, Enablement, and Analytics Community Leadership

Coach and support junior analysts: data preparation, modelling approaches, visualization standards, stakeholder management, and project execution.

Provide training and ongoing support to reporting champions and business users to ensure consistent and effective reporting usage.

Influence and support fellow analysts with problem resolution, documentation, and best-practice adoption.

8) Project and Program Contribution

Lead medium/tactical analytics projects, managing tasks, blockers, timelines, and stakeholder expectations with minimal oversight.

Participate in international programs and projects requiring analytics/BI expertise; put metrics in place to monitor success criteria and guide delivery decisions.

Provide detailed analysis and documentation to support project planning and resourcing; enable automation wherever feasible.

Represent the analytics function in governance discussions and ensure decisions are communicated clearly to the wider reporting community.

Requirements (Qualifications & Experience)

Education

Bachelor’s degree required; preference for qua