Build and scale advertiser-ready Media Mix Models (MMM)
Design, productize, and deploy granular Sponsored Product Ads MMM solutions that advertisers actively use for budget planning, scenario simulation, and optimization
Establish MMM as a core FY27 Joint Business Plan (JBP) measurement capability with strong adoption and credibility
Advance incrementality and causal measurement for Sponsored Ads
Lead the design and analysis of A/B tests and quasi-experimental frameworks to quantify true incremental lift from media investments
Move the organization beyond pre/post reporting toward trusted, decision-grade measurement that informs product launches and advertiser strategy
Develop machine learning models that improve advertiser growth and member experience
Build advertiser-level segmentation and retention models that drive supplier growth, along with member-level ad response prediction models that optimize performance while protecting the member experience
Own experimentation strategy for new ad products and formats
Partner with Product, Engineering, and Ads teams to design rigorous experiments for SPA product launches, interpret results, and translate findings into clear recommendations that influence roadmap and go-to-market decisions
Productize data science solutions at scale
Deliver production-grade modeling systems with robust pipelines, monitoring, and retraining
Apply AI-driven approaches to automate workflows, accelerate insights, and ensure measurement solutions are scalable, testable, and repeatable
Act as a technical thought partner across MAP
Lead cross-functional discussions on measurement and modeling tradeoffs, educate stakeholders, and provide clear, actionable guidance that balances analytical rigor with business impact

Requirements

A master’s degree or higher in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related quantitative field, plus 5+ years of industry experience applying advanced analytics and machine learning to real-world business problems
A proven track record of building and scaling production-grade data science systems, including model training pipelines, evaluation, monitoring, and retraining for decision-critical use cases
Deep hands-on experience with Media Mix Modeling (MMM) for budget planning and optimization, including saturation curves, diminishing returns, marginal ROAS, and Bayesian or frequentist approaches
Strong expertise in causal inference and experimentation, including A/B testing, power analysis, difference-in-differences, matched or synthetic controls, and Bayesian time series methods
Solid grounding in machine learning and statistical modeling, including advertiser segmentation, ad response prediction, uplift modeling, and bias–variance tradeoffs
High proficiency in Python and SQL, with experience working in large-scale data environments (e.g., BigQuery, Dataproc), distributed data processing, and feature pipeline development
Experience with TensorFlow or similar frameworks is preferred
Familiarity with modern analytics and measurement tooling, such as Google Meridian or similar MMM platforms, and GenAI / LLM-based tools for automation and scalable insight generation
A strong analytical mindset, comfort operating in ambiguity, and a collaborative, impact-driven approach to partnering with Product, Engineering, and Advertising teams

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

Media Mix Modelingmachine learningA/B testingcausal inferencestatistical modelingadvertiser segmentationad response predictionPythonSQLBayesian methods

Soft Skills

analytical mindsetcollaborativeimpact-drivencomfort operating in ambiguityclear communicationtechnical thought partnercross-functional leadershipeducational guidanceactionable recommendationsdecision-making