Manager, Delivery Strategy Analytics

Papa Johns • Atlanta, GA

1d

About The Position

We are seeking a highly analytical and execution‑focused team member to join our team as Manager, Delivery Strategy at Papa John’s. This role is responsible for supporting profitable growth across first‑party (native) and third‑party (aggregator) delivery channels through data‑driven analysis, economic modeling, and performance measurement. The Manager of Delivery Strategy will own the day-to-day analytical evaluation of delivery channel performance, including unit economics, demand drivers, pricing and fee structures, and promotional effectiveness. With a strong customer-first and margin-aware mindset, this individual will help balance consumer value, operational considerations, and P&L impact to drive transaction growth, customer satisfaction, and retention. This role will partner closely with Marketing, Operations, Finance, Technology, and external delivery platforms to translate insights into actionable recommendations. The Manager will support the development and execution of delivery strategies by analyzing tradeoffs between first-party and third-party channels, identifying incremental growth opportunities, and measuring true performance through experimentation and attribution frameworks. The ideal candidate is a strong analytical practitioner who thrives in ambiguity, brings rigor to decision-making, and can clearly communicate complex findings to cross-functional stakeholders in a fast-paced environment.

Requirements

• Education: Bachelor’s degree in Business Analytics, Statistics, Economics, Engineering, or Finance.
• Experience: 5+ years of experience in data-centric roles (e.g., Consulting, Data Science, or Revenue Management), specifically within the QSR or E-commerce sectors.
• Technical Stack: Advanced proficiency in SQL for data extraction and manipulation.
• Expertise in Tableau, Power BI, or Looker for executive-level data visualization.
• Advanced Excel/Google Sheets (Monte Carlo simulations, VBA, complex financial modeling).
• Advanced Quantitative Analytics: Deep expertise in applied statistics, cohort analysis, and causal inference to evaluate delivery performance, pricing strategies, and marketing effectiveness across first‑ and third‑party channels.
• Economic & Financial Modeling: Strong ability to build unit‑level contribution margin models, elasticity analyses, and scenario frameworks to quantify tradeoffs across fees, commissions, labor, and promotional spend.
• Marketing & Channel Analytics: Proven ability to analyze multi‑channel customer acquisition and retention performance, including attribution modeling, incrementality analysis, CAC/LTV modeling, and promotional ROI measurement.
• Experimental Design & Causal Testing: Hands‑on experience designing, executing, and analyzing A/B and multivariate experiments, with a strong command of statistical significance, confidence intervals, and lift measurement.
• Forecasting & Predictive Modeling: Skilled in time‑series analysis, demand forecasting, and driver‑based models to predict delivery volume and order mix at regional and store levels, enabling proactive operational planning.
• Data Engineering & Analytical Workflows: Comfortable working with large, messy datasets and managing analytical pipelines across SQL, BI tools, and modeling environments to ensure data accuracy, repeatability, and scalability.
• Insight‑Driven Storytelling: Exceptional ability to translate complex analyses into concise, executive-ready narratives and visuals, clearly communicating assumptions, methodology, and business impact.
• P&L‑First Decision Science: Strong focus on contribution margin optimization, leveraging data to balance growth, customer experience, and long‑term profitability.
• Cross‑Functional Analytical Leadership: Ability to partner with Operations, Marketing, Finance, and external platforms to align stakeholders on data definitions, assumptions, and analytically grounded decisions.
• High‑Velocity Execution: Thrives in fast‑paced, ambiguous environments, rapidly iterating on analyses, prioritizing highest‑impact work, and delivering actionable insights under tight timelines.

Nice To Haves

• Master’s degree (MS/MBA) in a quantitative field strongly preferred.
• Experience with Python or R for statistical modeling and predictive analytics is a significant plus.

Responsibilities

• Unit Economic Analysis: Build and maintain financial models to assess the profitability of first-party (native) versus third-party (aggregator) delivery channels, incorporating commissions, labor costs, delivery fees, and marketing contributions.
• Price Elasticity & Fee Optimization: Analyze customer response to delivery fees, service charges, and menu pricing to understand elasticity and inform fee structures that balance conversion and margin.
• Channel Attribution & Marketing Performance: Support the development of attribution analyses to evaluate incremental versus cannibalistic demand across delivery platforms (e.g., DoorDash, Uber Eats, Grubhub, and Papa Johns owned channels), including performance measurement of paid marketing, promotions, and customer acquisition efficiency.
• Demand Forecasting & Operational Support: Use historical order volume, seasonality, and promotional data to forecast delivery demand at the regional and store levels, partnering with Operations to inform labor planning and execution.
• Reporting & Dashboarding: Create and maintain Tableau / Power BI dashboards to track delivery and marketing KPIs such as CPA, CLV, conversion rates, order mix by channel, and delivery time variance.
• A/B Testing & Experimentation: Assist in the design and analysis of A/B tests for delivery pricing, fees, and promotions, measuring performance lift, ROI, and statistical significance.
• Third‑Party Partner Analysis: Analyze third‑party aggregator performance and operational metrics to identify trends, validate billing accuracy, and highlight SLA risks or optimization opportunities.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level