Senior Data Scientist, Workforce Management Analytics

Role overview A senior data science role embedded with a Workforce Management (WFM) team inside a customer support analytics function. The work centers on driving operational efficiency through accurate forecasting, scheduling, and intraday planning while quantifying how those actions affect the broader business.

Responsibilities - Build forecasting and time-series models to support scheduling and intraday decision-making. - Simplify forecasting processes and maintain the capacity planning framework. - Build and support KPI dashboards that surface operational performance. - Conduct root cause analysis to improve service-level agreement (SLA) outcomes. - Explore cost optimization opportunities through what-if scenario analysis. - Enhance the WFM data pipeline and uncover insights that refine planning, scheduling, reporting, and intraday management.

Requirements - 5+ years of experience in a data science or analytics position. - Hands-on experience building forecasting models using techniques such as Prophet, SARIMA, or Holt-Winters. - Experience with cost analysis and what-if scenario analysis. - Deep knowledge of statistical packages in Python or R. - Proficiency writing structured and efficient SQL queries against large datasets. - Ability to influence and advise senior stakeholders.

Nice to have - Experience applying machine learning to forecasting problems. - Experience working alongside ML engineers or on an ML platform. - Familiarity with building data pipelines using Airflow or similar orchestration tools.

Benefits and work setup - Flexible ways of working based in Berlin, including hybrid, in-office, or remote arrangements. - Opportunity to work on a large-scale platform with complex, real-time geolocational and temporal data. - Culture that emphasizes ownership, humility, and continuous learning, with a stated commitment to growing diverse, inclusive teams.