Staff, Data Scientist Sponsored Media Mix Modeling & Experimentation

Position Summary...

What youll do...

• 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.

• Amasters degree or higherin Computer Science Machine Learning Statistics Mathematics Operations Research or a related quantitative field plus5 years of industry experienceapplying advanced analytics and machine learning to real-world business problems.
• A proven track record ofbuilding 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 with the ability to translate outputs into clear business recommendations.
• Strong expertise incausal inference and experimentation including A/B testing power analysis difference-in-differences matched or synthetic controls and Bayesian time series methods with at least2 years of hands-on causal ML or experimentation experience.
• Solid grounding inmachine learning and statistical modeling including advertiser segmentation ad response prediction uplift modeling and biasvariance tradeoffs; exposure to keyword or bidding optimization is a plus.
• High proficiency inPython 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 withmodern 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.
• Retail Media or Sponsored Product Ads experienceis a plus as is prior experience building scalable media measurement or targeting solutions.