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European Wax Center · 5 hours ago

Director of Data Science

Plano, TX

Full-time

Onsite

Director/Executive

10+ years exp

European Wax Center is seeking a Director of Data Science responsible for advanced analytics, modeling, and operational insight across the company. This role involves owning the operations analytics and data science roadmap, partnering with Operations leadership, and ensuring data is converted into actionable operational insights.

BeautyCosmetics

No H1B

Responsibilities

Own the operations analytics and data science roadmap, prioritizing execution, efficiency, and consistency over topline growth

Develop and oversee models and analytical frameworks including: Labor planning, productivity, and staffing optimization, Capacity utilization and appointment / service scheduling analysis, Service cycle time, throughput, and bottleneck identification, Field execution consistency and franchise compliance analytics, Location-level performance benchmarking and variance analysis

Apply statistical methods to identify root causes of operational underperformance and quantify improvement opportunities

Partner with Operations, Field Leadership, and Training teams to: Define operational KPIs and success metrics, Translate analytical findings into clear operational actions, Support rollout, adoption, and performance tracking of operational initiatives

Support operational pilots and initiatives with: Baseline measurement, Test-and-learn frameworks, Post-implementation performance evaluation

Serve as the primary analytics partner to FP&A for operational performance, supporting: Labor cost analysis and productivity metrics, Unit-level and franchise-level cost drivers, Scenario modeling related to staffing, utilization, and efficiency, Ensure operational analytics align with financial definitions and cost structures, enabling consistent reporting and decision-making, Support executive and board-level materials with fact-based, operational performance insights

Own analytical definitions, business logic, and semantic consistency for operations KPIs, including: Labor efficiency and utilization, Service-level adherence and cycle times, Compliance, rework, and quality indicators, Training effectiveness and operational readiness, Partner with Data Engineering to ensure these definitions are implemented accurately in curated datasets, semantic layers, and BI assets

Collaborate closely with Data Engineering on: Analytical data requirements and use cases, Data availability for operational systems (e.g., scheduling, labor, POS, field tools), Data quality validation and exception thresholds, Act as the downstream analytical consumer and validator of engineered data, ensuring it supports operational modeling and decision-making

Design and oversee executive and operations dashboards that clearly show: Where execution is breaking down, Which locations or franchises require intervention, Whether operational initiatives are delivering expected outcomes, Enable self-service analytics for Operations and Field teams while maintaining analytical rigor and consistency

Ensure operational analytics and models are well-documented, reproducible, and SOX-compliant

Establish standards for analytical validation, documentation, and model governance, Partner with Finance, Internal Audit, and Operations leadership to ensure analytics are audit-ready and decision-grade

Establish best practices for: Analytical rigor and statistical validity, Clear operational storytelling and insight delivery, Model documentation and peer review, Build a culture that prioritizes execution, accountability, and measurable operational improvement

Qualification

Statistical analysisOperational analyticsSQLPythonBI platformsKPI designForecasting techniquesOptimization techniquesLeadershipCollaborationCommunicationProblem-solving