Director of Data Science

EWC Corporate LLC•Plano, TX

About The Position

The Director of Data Science is responsible for advanced analytics, modeling, and operational insight across the company. This role owns how operational data is derived, analyzed, modeled, governed analytically, and translated into actionable intelligence to improve execution across field operations, and franchisee locations. Reporting to the VP of FP&A, the role serves as the analytical backbone for operations, enabling better labor planning, capacity utilization, service quality, compliance, and consistency across a large, distributed franchise system. The role requires strong business and financial fluency but is not a finance position and does not own accounting, revenue management, or financial systems. The Director partners closely with Operations leadership and a centralized Data Engineering team to ensure engineered data is converted into actionable, scalable, and explainable operational insights that improve unit-level performance and franchise outcomes.

Requirements

Bachelor’s degree in Statistics, Economics, Engineering, Mathematics, Computer Science, or related field
10+ years of experience in data science, operations analytics, or applied statistics, with 5+ years in leadership roles
Deep expertise in: Statistical analysis, forecasting, and optimization techniques Operational analytics and KPI design SQL for analytical modeling and validation Python, R, or similar analytical languages BI and visualization platforms (e.g., Power BI, Tableau, Looker) Strong understanding of operational and cost drivers (labor scheduling, throughput, utilization, quality, compliance) Experience in multi-unit, franchise, retail, or field-based operating models
Proven ability to translate analytics into practical operational actions

Nice To Haves

Experience supporting Operations, Supply Chain, or Workforce Management teams
Familiarity with public-company or SOX-controlled environments
Experience partnering with FP&A without owning financial processes

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