Goal of function:

To support the CDI workforce & productivity optimization efforts by reviewing and monitoring CDI clinics’ performance in relevant KPIs as well as conducting comprehensive data quality checks and analyses.

Main tasks:

• Monitor staffing levels, clinic workforce costs and productivity in our CDI clinic network and detect & escalate deviations

• Perform data quality checks and address issues on all country reported, relevant Staffing & Productivity KPI’s for Balanced Scorecard, Operational Review Dashboard

• Deep dive into specific reports and data sets on clinic schedules, shift duration & labour utilization and highlight outliers

• Support harmonization of staffing blueprints within CDI

• Support country users of Workforce Management Suite (WMS) in regular use of reports and efficiency management functionalities

Key Expectations & Responsibilities

• Analytical and Structured Thinking: Must be highly analytical, enjoy working with data, structured in approach, and practical in execution.

• Excel Proficiency: Advanced skills in Excel and comfort with handling large datasets are crucial.

• Fast Learning Curve: While specific domain knowledge can be taught, the person should be a quick learner.

• Autonomous but Coordinated: Will work under supervisor but expected to operate quite independently.

• Strong Communication: Needs to communicate effectively, with diplomacy and assertiveness, especially when interacting with country teams.

• Problem-Solving & Curiosity: Should be able to investigate and understand discrepancies in reports, such as unusual productivity figures.

• Tolerance to Routine: The work involves repetitive data patterns; the ideal candidate must not be averse to routine.

• Consultant-like Attitude: Assertiveness without conflict; capable of presenting insights in a calm, persuasive manner.

• Language & Location: Strong English required.

Ideal Background

• 2–3 years’ experience in shared service centers or similar environments (e.g., controlling, reporting, or financial data analysis).

• Exposure to large Excel files and complex datasets.

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