Note: The job is a remote job and is open to candidates in USA. Yahoo serves as a trusted guide for hundreds of millions of people globally, helping them achieve their goals online through our portfolio of iconic products. 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.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- Incentive compensation opportunities in the form of discretionary annual bonus or commissions
- Equity incentives
- Healthcare
- A great 401k
- Backup childcare
- Education stipends
- Flexible hybrid work options that our employees love
- 11 employee resource groups (ERGs) enhance a culture of belonging with programs, events and fellowship that help educate, support and create a workplace where all feel welcome
Company Overview
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