Sr. Director, Data Science & Experimentation

A Little About Us

Our team is focused on accelerating growth across Yahoo's consumer portfolio by bringing together performance marketing, media, data science, and experimentation. We build the measurement, experimentation, and technical capabilities that help drive customer acquisition, engagement, and long-term growth.

Position Overview

As the Sr. Director of Data Science \& Experimentation, you'll build and lead the quantitative backbone that powers marketing decisions across Yahoo's Growth \& Media organization. From experimentation and causal inference to predictive modeling and real-time audience signals, your team will help shape how we measure performance, optimize investment decisions, and improve customer acquisition and engagement.

This role is for a builder who is equally comfortable designing the statistical architecture of a major experiment and translating complex findings into clear recommendations for senior business leaders. You'll partner closely with Performance Marketing, Media Planning, and technical product leadership to integrate machine learning models, experimentation capabilities, and intelligent automation into Yahoo's growth marketing infrastructure.

What You'll Do

• Own the experimentation platform and methodology for the Growth \& Media team, including A/B testing, incrementality studies, geo holdouts, matched market tests, and multi-armed bandit frameworks that improve decision speed.
• Build and lead a team of data scientists and analysts focused on growth measurement, marketing attribution, audience modeling, customer lifetime value prediction, and forecasting.
• Develop causal inference frameworks that measure the true incremental impact of paid, organic, and owned marketing programs, moving beyond last-click attribution to more rigorous measurement.
• Architect and oversee the data pipelines, feature stores, and model-serving infrastructure that power real-time campaign optimization and audience targeting.
• Design and deploy predictive models, including propensity, customer lifetime value, churn, recommendation, and lookalike audience models that inform budget allocation, targeting, and retention strategies across channels.
• Work closely with technical product leadership on agentic growth tooling to embed machine learning signals and model outputs into autonomous marketing workflows, enabling agents to act on high-quality data.
• Establish and scale the team's experimentation culture by increasing testing velocity, improving statistical rigor, and building tools that make experimentation a core part of how the team operates.
• Present complex analytical findings and strategic recommendations in a clear, actionable way for senior leadership.
• Evaluate emerging technologies, including AI-powered capabilities, where they can improve marketing performance, operational efficiency, or decision-making.

What You Bring

• 12\+ years in data science or applied machine learning, with 4\+ years building and leading high-performing data science or analytics teams.
• Deep expertise in causal inference and experimentation design, including A/B testing, difference-in-differences, holdout methodologies, and Bayesian experimental frameworks.
• Strong machine learning background with experience building, validating, and deploying propensity models, forecasting models, recommendation systems, or audience models in production.
• Proficiency in Python and SQL, with experience using distributed computing frameworks such as Spark, BigQuery, Databricks, or equivalent technologies.
• Experience building or meaningfully scaling an experimentation platform within a consumer internet or marketing-intensive organization.
• Proven ability to translate complex model outputs and statistical findings into clear business recommendations for technical and non-technical audiences.
• Track record of embedding data science into day-to-day marketing and product decisions through close partnership with cross-functional teams.

Preferred Qualifications

• Experience with marketing mix modeling (MMM) and multi-touch attribution (MTA) at scale.
• Familiarity with agentic AI systems and how machine learning signals support autonomous, real-time decision making in marketing workflows.
• Advanced degree (MS or PhD) in Statistics, Computer Science, Applied Mathematics, or a related quantitative field.

What Success Looks Like

Success in this role means establishing scalable experimentation and measurement capabilities that help teams make smarter marketing decisions. You'll improve how the organization measures marketing effectiveness, increase experimentation velocity, and embed machine learning into marketing workflows in ways that create meaningful business impact.