data analyst in hospitality

The company is starting a labour optimisation programme with its Operations team, alongside FP&A and Data Science, to improve wage percentage to sales while maintaining service standards. It is selecting an advanced scheduling platform, with the role owning the analytical foundation for measuring productivity, setting labour standards and assessing demand forecast requirements.

• Identify site-by-site labour efficiency opportunities and size them
• Work with Operations and Finance to trial scheduling changes and measure their impact
• Build labour reporting that enables Business Partners and GMs to monitor productivity, schedule changes and shift adherence daily and weekly
• Support Finance in translating demand forecasts into labour requirements and building models that convert covers, check-ins, treatments and occupancy into hours by role
• Partner with Data Science and Operations to assess forecast accuracy and recommend improvements
• Connect labour deployment to member experience and demonstrate that productivity gains do not come at its expense

• Proven experience in labour optimisation, with personally delivered measurable improvements in wage percentage or sales per labour hour in a multi-site operation, and the ability to explain the levers used, how opportunities were sized and how results were proven
• Track record of partnering with finance and operational stakeholders to implement change, not just report on it
• 5+ Years in an analytics role and genuine multi-site operational exposure in hospitality, retail, leisure or QSR
• Advanced SQL and comfort with a modelled data warehouse
• Strong financial modelling in Excel and fluency in P&L mechanics, including wage percentage, flow-through and contribution
• BI development experience with Omni, Looker, Tableau or Power BI, building tools operators use
• Experience with rota, time and attendance, and payroll data, and a realistic understanding of their challenges
• Ability to critically evaluate demand forecasts for accuracy, bias and seasonality without needing to build them
• Будет плюсом: Snowflake and dbt, workforce management or scheduling platforms such as Unifocus, HotSchedules, Dayforce, Fourth, S4 Labour, UKG or Quinyx, understanding of international labour regulations (particularly US predictive scheduling and fair workweek rules, overtime and break requirements), labour standards or time-and-motion experience, Python for analysis or AI tooling such as Claude Code, experience linking labour deployment to customer experience outcomes

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