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LaunchDarkly is seeking a Head of Experimentation to lead the company's experimentation product pillar as it converges with feature management and AI-native workflows. This is a VP-level product leadership role with direct oversight of the Product team, operating in a triad model with Engineering and Design counterparts.
The role owns the strategic direction and commercial outcomes of the experimentation platform across three key areas: (1) the in-product experimentation experience, (2) warehouse-native analysis capabilities, and (3) the infrastructure that scales them. You will be accountable for closing the gap with sophisticated data organizations—winning high-maturity buyers who demand advanced statistical rigor, warehouse coverage, and experiment-first workflows.
Key responsibilities include:
- Direct leadership of the Product team; accountable for strategy, roadmap delivery, and commercial outcomes
- Positioning experimentation as the measurement layer for the AI software development lifecycle, partnering with AI product, observability, and core feature management leaders
- Winning competitive evaluations against sophisticated data science and PM buyers by delivering statistical depth, warehouse coverage, and advanced analysis capabilities
- Expanding warehouse-native coverage across major data platforms with parity on analysis-only mode, variance reduction, ratio/percentile metrics, exposure validation, and arbitrary-window analysis
- Running a disciplined, high-performing function with predictable quarterly delivery, AI-assisted engineering productivity, and strategic hiring
- Serving as the external face of the experimentation category, credibly representing the product to data scientists, PMs, analysts, and partners
Success is measured by: win rate on experimentation-involved deals (especially competitive head-to-head), reference-grade customers at the top maturity curve, monthly active customer and ARR growth, experimentation attach rate on enterprise deals, and engineering throughput/roadmap velocity.
Required qualifications: Senior product leader (GM, VP, or equivalent) with a track record owning a product line competing on statistical rigor and data infrastructure. Deep operator-level fluency in experimentation methodology including causal inference, variance reduction, ratio metrics, sequential testing, exposure design, multi-armed bandits, and composite metrics at production scale. Credibility with data science leaders at sophisticated organizations. Experience leading cross-functional teams (engineering, design, data science) with multi-quarter roadmap ownership and board-level reporting. Fast decision-maker who ships and learns. Opinionated about experimentation in an AI-native world, particularly how agents and autonomous systems will use experimentation infrastructure differently.
Preferred: Built or scaled experimentation as core infrastructure (not secondary analytics). Won competitive evaluations where data science organizations were the deciding voice. Shipped warehouse-native data products with operational experience running experiments against customer data infrastructure.
