Data Scientist, Marketing Analytics

AutoZone

Memphis (TN)

On-site

USD 90,000 - 130,000

Full time

14 days+

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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

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

SQL

Job description

This role will partner closely with external data vendors and media partners, lead geo-experimentation
design and measurement, and develop propensity and optimization models that guide media investment
decisions. We value continuous learning, rigorous causal thinking, and the ability to translate complex
analysis into clear, actionable recommendations for marketing leaders.

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. Build MMM in-house to measure the return on investment (ROI)
across different advertising channels. Translate MMM and incrementality outputs into
media/channel budget allocation recommendations, working with internal stakeholders to connect
model output to planning cycles
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

Qualifications

Experience: 3–5 years in data science, marketing analytics, or applied statistics, ideally with
direct exposure to marketing measurement in a retail, or omnichannel environment
Technical Skills: 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, decisionready 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

Since opening our first store in 1979, AutoZone has grown into a leading retailer and distributor of automotive parts and accessories across the Americas. Our customer-first mindset and commitment to Going the Extra Mile define who we are, for both our customers and AutoZoners. Working at AutoZone means being part of a team that values dedication, teamwork, and growth. Whether you're helping customers or building your career, we provide tools and support to help you succeed and drive your future.

Benefits at AutoZone

AutoZone offers thoughtful benefits programs with one‑on‑one benefits guidance designed to improve AutoZoners’ physical, mental and financial well‑being.

All AutoZoners (Full-Time and Part-Time):

Competitive pay
Unrivaled company culture
Medical, dental and vision plans
Exclusive discounts and perks, including an AutoZone in‑store discount
401(k) with company match and Stock Purchase Plan
AutoZoners Living Well Program for free mental health support
Opportunities for career growth

Additional Benefits for Full-Time AutoZoners:

Paid time off
Life, and short- and long-term disability insurance options
Health Savings and Flexible Spending Accounts with wellness rewards

Minimum age requirements may apply. Eligibility and waiting period requirements may apply; benefits for AutoZoners in Puerto Rico, Hawaii, or the U.S. Virgin Islands may differ. Learn more about all that AutoZone has to offer at Careers.AutoZone.com.

We proudly support Veterans, Active‑duty Service Members, Reservists, National Guard and Military Families. Your experience is highly valued, and we encourage you to apply to join our team.

AutoZone, and its subsidiary, ALLDATA are equal opportunity employers. All applicants will be considered for employment without attention to age, race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status, or any other legally protected categories.

Job Info