Senior Data Scientist, Product Analytics — Yahoo.com/Homepage

It takes powerful technology to connect our brands and partners with an audience of hundreds of millions of people. Whether you’re looking to write mobile app code, engineer the servers behind our massive ad tech stacks, or develop algorithms to help us process trillions of data points a day, what you do here will have a huge impact on our business—and the world.

A Little About Us

The Yahoo homepage is one of the most visited destinations on the internet. Our Yahoo News Group Analytics Team is the engine that powers its evolution, using rigorous experimentation and advanced analytics to improve the daily habits of millions of users. We are a high-autonomy team that values statistical integrity, clear communication, and an AI-forward approach to solving complex product problems.

About the Role

We're hiring a Senior Data Scientist to lead product analytics and experimentation for the Yahoo.com homepage. In this role, you will define how success is measured, design high-stakes experiments, and generate insights that directly shape our product roadmap. You'll partner closely with Product, Engineering, Design, and Data Engineering to guide high-impact decisions. Your work will span the full analytics lifecycle - from metric definition and instrumentation to experimentation and deep-dive analysis.

This is a hands-on senior individual contributor role with significant ownership. You will operate as the analytical lead for your area, bringing structure to highly ambiguous problems and ensuring decisions are grounded in rigorous analysis. The person best suited for this role will be comfortable with vague metrics and energized by ambiguous problems. You've owned experimentation programs end-to-end — not just contributed to them — and you know the difference between a statistically significant result and a meaningful one. You treat AI tools as a core part of your workflow, not an add-on, and you're always looking for ways to work more efficiently without sacrificing rigor.

Note: This role is focused on product analytics and statistical modeling. It is not an ML engineering role—you will not be responsible for building or productionizing machine learning systems.

What You'll Do

• Influence product strategy and roadmap through data-driven storytelling, partnering closely with Product, Engineering, and Design.

• Define and own key product metrics (e.g., activation, engagement, retention) and the measurement frameworks behind them.

• Design, execute, and interpret complex, high-stakes A/B tests to inform product roadmaps.

• Proactively identify and implement AI-driven efficiencies in day-to-day analytics, automating repetitive SQL, Python pipelines, and report generation using approved tools like Gemini and GitHub Copilot.

• Apply advanced statistical methods (e.g., regression, cohort analysis, causal inference, churn/retention modeling) to solve complex business problems.

• Actively validate and QA AI-generated analytical scripts, database queries, and code blocks to maintain statistical integrity, error-checking for logical consistency and hallucinations.

• Partner with Data Engineering to build reliable, scalable datasets and establish robust event instrumentation.

• Lead and mentor junior analysts and project workstreams, championing best practices in experimentation, data quality, and statistical rigor across the team.

• Build dashboards and scalable data assets that enable self-service analytics across the organization.

What We’re Looking For

• 5+ years of experience in product analytics, data science, or a related quantitative field with ownership of metrics and experimentation at scale.

• Strong statistical foundation, including hypothesis testing, regression, and causal inference.

• Demonstrated experience leveraging generative AI tools (e.g., Gemini, Claude) or AI-assisted coding tools (e.g., GitHub Copilot) to accelerate coding, debugging, and analytical workflows.

• Experience partnering with cross-functional teams to drive high-impact decisions through A/B testing and behavioral analysis.

• Proficiency in SQL and Python or R for data analysis and visualization.

• Experience partnering with Data Engineering to build reliable datasets and event instrumentation.

• Strong understanding of data modeling for both ad hoc and scalable analytics.

• Comfort operating in evolving, AI-augmented