Build and scale advanced measurement solutions
Design, deliver, and continuously improve multi-touch attribution and media mix modeling solutions that scale across suppliers, categories, and channels using modern ML techniques
Ensure attribution and modeling outputs clearly quantify incremental sales and iROAS, enabling MAP to influence budget allocation decisions that drive incremental ad revenue
Lead the refinement and operationalization of Omni Impact MTA using online and offline data, ensuring it can be refreshed, rescored, and reused efficiently without custom rebuilds
Create and operationalize an Enhanced MMM framework that supports budget planning and optimization, integrates MTA outputs as priors, and materially reduces processing and turnaround time
Establish repeatable processes for annual builds and biannual or quarterly refreshes that maintain consistency, transparency, and alignment with prior reporting
Advance modeling logic to deliver brand- and media-type–level insights (e.g., onsite display, social, offsite), enabling suppliers to clearly see what drives performance
Integrate new media channels and data sources (ISB, TV Wall, social platforms, Freeosk, enterprise marketing assets) into attribution and MMM frameworks
Enhance GenAI-driven insight generation that automates storytelling around incremental sales, iROAS, and path-to-purchase—making advanced analytics easier to consume and act on
Partner with data engineering to eliminate manual workflows, modernize pipelines, structure codebases for reuse, and maintain strong documentation and governance standards
Serve as the analytics authority for internal stakeholders and suppliers, supporting executive readouts, case studies, seasonal analyses, and strategic measurement conversations

Requirements

A proven track record building and scaling MMM and MTA solutions that influence media investment decisions and drive measurable revenue impact
Deep expertise in advanced analytics, ML, and statistical modeling within a retail media, advertising, or data-rich environment
Strong programming skills in Python and SQL, with hands-on experience in Databricks, BigQuery, and GitHub-based development workflows
Experience designing production-ready data pipelines that integrate online, offline, social, and in-store data sources
Practical experience applying Generative AI to analytics—particularly for automating insights, interpretation, or narrative generation
A "process engineer" mindset with a bias toward automation, scalability, and repeatability over one-off solutions
The ability to translate complex analytics into clear business value for sales teams, suppliers, and executive leadership
Comfort operating in ambiguity, with the curiosity and drive to continuously improve how measurement works
Familiarity with retail, membership-based businesses, or retail media networks (preferred)
AI/ML engineering experience building scalable measurement or targeting systems (strong plus)
Master’s degree or PhD in a quantitative field such as Statistics, Economics, Computer Science, Mathematics, or similar (preferred)

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

multi-touch attributionmedia mix modelingmachine learningstatistical modelingPythonSQLdata pipelinesGenerative AIanalyticsdata integration

Soft Skills

process engineeringautomationscalabilityrepeatabilitycommunicationcuriositydrive for improvementtranslating analyticsleadershipcollaboration

Certifications

Master’s degreePhDStatisticsEconomicsComputer ScienceMathematics