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European Wax Center · 2 hours ago

Director of Data Science

Corporate Office TX, Plano, TX, US

Full-time

Onsite

Director/Executive

10+ years exp

European Wax Center is seeking a Director of Data Science to lead advanced analytics and operational insights for the company. This role focuses on optimizing field operations and franchisee performance through data-driven decision-making and collaboration with various teams.

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 platformsForecasting techniquesKPI designROperational decision supportTeam developmentAnalytical storytellingLeadershipCollaboration