WFM Capacity Planning Senior Analyst

About the role

Wise is looking for a WFM Capacity Planning Senior Analyst in Tallinn. As the strategic owner of the workforce supply-and-demand ecosystem, you will move beyond traditional spreadsheets to build a predictive, financially aligned and highly optimised capacity function.

Key responsibilities

• Own the end-to-end capacity planning process, set standards for planning horizons and documentation, and review and approve plans built by Planning Analysts.
• Build forecasting models using statistical techniques and tools such as Python, R and SQL, and define risk bands under uncertainty.
• Act as the primary WFM owner for inputs into the financial rolling forecast, working with Finance to align staffing models with corporate financial targets and cost objectives.
• Lead demand validation discussions with senior Operations, Analytics and Demand Management leaders, challenging assumptions with data to secure formal plan sign-offs.
• Design "what-if" scenarios covering demand volatility, hiring delays and productivity shocks, giving executives clear options and trade-offs.
• Translate capacity plans into hiring strategies with Talent Acquisition, hand over staffing requirements to Scheduling, and work with Analytics on automated data pipelines.
• Mentor Planning Analysts and coach them on analytical rigor, financial thinking and stakeholder management.

Required profile

• 4-7 years of experience in Workforce Management, Capacity Planning, Forecasting, Data Science or Operations Analytics.
• Proven track record of owning planning cycles, managing financial alignment and driving stakeholder sign-offs in complex, high-volume, multi-skill environments.
• Ability to challenge assumptions with evidence, lead difficult trade-off conversations and influence senior stakeholders without direct authority.
• Structured, analytical, forward-looking and calm under pressure.

Required skills

• Predictive or statistical analysis with Python, R or equivalent environments (time-series or regression-based modelling).
• Strong SQL, including complex queries, data modelling and joining multiple operational datasets.
• Advanced spreadsheet skills and a bias toward automation.