Head of Experimentation

Responsibilities

• Own the Experimentation pillar, providing direct leadership to Product and partnering with Engineering and Design counterparts in a triad model.
• Set the pillar strategy, deliver the roadmap, and own commercial outcomes across the in-product experimentation experience, warehouse-native analysis layer, and scaling infrastructure.
• Make experimentation the measurement layer of the AI software development lifecycle by connecting offline evaluation, production experiments, automatic promotion and rollback, and feedback loops for agents.
• Win sophisticated data organizations by defining and delivering statistical depth, warehouse coverage, and experiment-first workflows.
• Expand warehouse-native capabilities across major data warehouses and query layers, including analysis-only mode, variance reduction, ratio and percentile metrics, exposure validation, and arbitrary-window analysis.
• Run a high-performing function with disciplined roadmaps, predictable quarterly delivery, AI-assisted engineering productivity, and targeted hiring.
• Represent the product externally to data scientists, product managers, experimenters, analysts, and partners; translate strategy for the field and equip Sales for competitive evaluations.

Requirements

• Senior product leader at the GM, VP, or equivalent level with experience owning a product line competing on statistical rigor and data infrastructure.
• Operator-level fluency in experimentation methodology, including causal inference, variance reduction, ratio metrics, sequential testing, exposure design, multi-armed bandits, and composite or multi-objective metrics.
• Experience operating experimentation at scale against production data warehouses and non-deterministic systems where output variance affects sample-size and significance decisions.
• Credibility with data science leaders and experimentation specialists at sophisticated organizations, with the ability to recruit them.
• Experience leading a function spanning engineering, design, and data science.
• Ability to set multi-quarter roadmaps, allocate investments, and report results to executive teams and boards.
• Clear, direct communication and fast decision-making with incomplete information.
• Strong bias toward shipping and learning over lengthy requirements documentation.
• A well-developed perspective on experimentation in an AI-native world, including how agents and autonomous systems use experimentation infrastructure.

Nice-to-haves

• Experience building or scaling experimentation as core infrastructure rather than a secondary analytics capability.
• Experience winning competitive evaluations where a sophisticated data-science organization was the deciding voice.
• Experience shipping warehouse-native data products and operating experiments directly against customer data infrastructure.
• Belief that experimentation provides the evidence layer for proving changes built by people or AI agents work.

Compensation and Benefits

• Target pay ranges for Level M5, inclusive of a 20% bonus:
  • Zone 1: $301,000–$414,000
  • Zone 2: $271,000–$373,000
  • Zone 3: $256,000–$352,000
• Compensation varies based on skills, experience, and location.
• Benefits include restricted stock units, health, vision, dental, and mental health benefits.