Director - Pricing Strategy & Revenue Science

Director, Pricing Strategy & Revenue Science

Build the Future of Pricing at PODS

At PODS, we’re transforming how pricing drives growth, customer experience, and competitive advantage across our business. We’re looking for a visionary, analytically-driven leader to modernize and elevate our pricing capabilities from a primarily manual, rules-based model into a sophisticated, data-driven pricing function that improves conversion, margin and network utilization across every customer interaction.

As theDirector of Pricing Strategy & Revenue Science, you will lead the development and application of models that explain and forecast demand, conversion, price sensitivity, utilization, and revenue performance. Responsibilities include designing pricing and business experiments; building demand and revenue forecasts; developing optimization approaches that balance conversion, margin, and capacity; and creating analytical frameworks that quantify the impact of geography, mileage, seasonality, inventory availability, customer segment, competitive conditions, and other business drivers.

This leader will establish standards for causal measurement, model validation, data quality, reproducibility, monitoring, and post-deployment performance. The role partners closely with Product, Engineering, IT, Finance, Operations, Marketing, and other business teams to translate analytical work into scalable decision tools and operating processes. The Director will also lead root-cause analysis when performance shifts, communicate recommendations to senior leaders, and coach a high-performing team of data scientists and analytical professionals.

What You’ll Do

MANAGEMENT & SUPERVISORY RESPONSIBILTIES

• Reports to Vice President, Digital
• Directly manage the Pricing & Revenue Science Team, including Data Scientists and pricing/revenue analytics professionals. Full management authority includes hiring, performance management, compensation decisions, and terminations. Responsible for team priorities, analytical standards, and vendor/partner oversight as applicable.

Day to day, you will:

• Deep experience leading applied data science or decision science work in pricing, revenue management, forecasting, optimization, marketplace economics, or a related analytical domain
• Strong hands-on fluency in SQL and Python; able to review complex queries, notebooks, model code, and analytical pipelines. Experience with R or another statistical language is a plus
• Strong command of statistics and applied econometrics, including regression, hypothesis testing, causal inference, experimental design, measurement, and model validation
• Experience with predictive modeling, machine learning, forecasting, elasticity modeling, and optimization; able to evaluate methodology, feature selection, validation approaches, explain ability, and business applicability
• Familiarity with modern data science tooling and environments, including the Python analytics ecosystem (e.g., pandas, Num Py, scikit-learn, stats models or equivalents), cloud data warehouse/lake platforms, and Git/version control
• Ability to partner with Engineering and IT to product ionize analytical models and decision systems, with working knowledge of data pipelines, APIs, model monitoring, drift, data quality, and deployment lifecycle practices
• Ability to translate complex technical findings into clear business recommendations and influence senior leaders and cross-functional stakeholders using data-driven insights
• Proven track record leading high-impact analytical initiatives from discovery through implementation and developing data scientists and analysts with strong standards…