Marketing Analytics Scientist: MMM & Experimental Design

AutoZone; Memphis (TN); On-site; USD 90,000 - 130,000; Full time

Benefits offered by this job

Competitive pay

Unrivaled company culture

Medical, dental and vision plans

AutoZone in-store discount

401(k) with company match and Stock-Pu

AutoZoners Living Well Program for for

Job summary

AutoZone seeks a data analytics professional to partner with data vendors and media partners, lead geo-experimentation design and measurement, and develop propensity and optimization models guiding media investments. The role emphasizes rigorous causal thinking and translating analysis into actionable recommendations for marketing leadership.

You will build MMM in-house, design experiments, and collaborate to operationalize models at scale, with a focus on driving measurable ROI across

Qualifications

• 3–5 years in data science, marketing analytics, or applied statistics, ideally with direct exposure to marketing measurement in a retail, or omnichannel environment
• Proficiency in Python and/or R and SQL; hands-on experience with causal inference methods, Bayesian modeling, experimental design and power analysis, and marketing mix modeling concepts
• Familiarity with Excel, cloud data warehouses and BI/visualization tools (Tableau, Looker, GCP, CDP)
• Dynamic environment: comfort working in a research-oriented group with multiple concurrent projects
• Communicate for impact: ability to translate statistical and modeling work into clear, decision-ready recommendations for non-technical executive stakeholders
• Educational Background: Bachelor's degree in statistics, information systems, data science, applied mathematics, economics, computer science, or related discipline; Master's degree preferred

Responsibilities

• Marketing Mix Modeling: Serve as the internal analytical counterpart to our Marketing Mix Modeling vendor, reviewing quarterly model refreshes for accuracy, geographic granularity, variable specification, and channel decay assumptions, and reconciling MMM outputs against internal experimental results
• Experimentation & Measurement: Design and analyze geo-experiments, media heavy-up/heavy-down designs, and matched-market or synthetic control tests across channels, including market selection, statistical power/MDE calculations, and exclusion protocols for confounding risk e.g., seasonal exposure, outlet share, promotions, etc.
• Build Optimization Frameworks: Design randomized holdout structures to measure incremental value of Email/SMS programs, and develop send-time, frequency, and content optimization models in partnership with other marketing entities
• Develop Propensity & Customer Models: Build propensity-to-purchase, churn, and channel-response models using transactional, loyalty, and behavioral data to support targeting, personalization, and customer segmentation
• Analyze Trends & Validate Data: Interpret customer and media performance data for actionable patterns, assess new data sources for improving model(s) accuracy, and maintain rigorous data validation standards
• Collaborate for Implementation: Partner with internal teams in advanced analytics work to operationalize models and ensure measurement infrastructure scales

Skills

Python

R

SQL

Causal inference

Bayesian modeling

Experimental design

MMM concepts

Excel

Cloud data warehouses

BI tools

Tableau

Looker

GCP

CDP

Education

Bachelor's degree in statistics, information systems, data science, applied mathematics, economics, computer science, or related discipline

Master's degree preferred

Tools

Python

R

SQL

Job description

AutoZone seeks a data analytics professional to partner with data vendors and media partners, lead geo-experimentation design and measurement, and develop propensity and optimization models guiding media investments. The role emphasizes rigorous causal thinking and translating analysis into actionable recommendations for marketing leadership.

You will build MMM in-house, design experiments, and collaborate to operationalize models at scale, with a focus on driving measurable ROI across