We're doing an AI-first engineering rebuild for a company that already has an audience of 100M+ people. This is a zero-to-one build with no legacy constraints. You're not here to run reports or maintain someone else's dashboards. You're here to turn ambiguous product questions into clear, data-backed answers that directly shape what we build and how we grow. You'll work alongside engineers and product teams, using AI to move faster than a traditional data science workflow ever allowed. The Product You'll work as part of a small, fast-moving team on the data foundation that powers product decisions across consumer products, media, gaming, and e-commerce. The work spans the full journey from raw user behavior to actionable insight. That means: Product questions that are ambiguous, high-stakes, and time-sensitive. You need to frame them correctly before you can answer them. A/B experiments where the design, statistical rigor, and interpretation directly determine what ships next. A fast-moving team that moves at a pace where every analysis needs to count, and AI tools are a core part of how we get there. What You'll Do Design and execute analyses that turn raw user behavior into actionable product insights, working closely with engineers and product teams to frame the right questions. Own A/B experiments end to end, from experiment design and statistical analysis through to result interpretation and recommendation. Communicate findings clearly to product and engineering stakeholders, translating complex analysis into actionable recommendations that drive product decisions. Leverage AI tools to accelerate your workflow, from data exploration and analysis to insight delivery and iteration. Who You Are AI-Native: You're already using AI tools daily to move faster, from writing queries and building models to exploring data and communicating findings. Analytical Builder: +3 years of hands-on experience in data science or analytics, with strong SQL, Python, and statistical modeling skills. You don't just analyze. You build the tooling and frameworks that make analysis repeatable and reliable. Product Connected: You care about why you're analyzing something, not just how. You can translate vague product questions into tractable data problems and communicate findings clearly to non-technical stakeholders. Experimentally Rigorous: You understand the principles of A/B testing, know where experiments go wrong, and hold the line on statistical rigor even under deadline pressure. Strong SQL and Python skills, experience with A/B experimentation frameworks, and familiarity with at least one major cloud data stack (AWS, GCP, Databricks, Snowflake, or equivalent). Bonus points for experience in consumer products, media, gaming, or e-commerce, and prior work alongside product or engineering teams in a fast-moving environment. Compensation: $178,000 –$223,000 base + equity + benefits Hybrid Model: Expected ~3 days per week in-office (San Mateo, CA)