Head of Experimentation

Feature management and experimentation have converged into a single market, and the buying dynamic at the top has shifted. Engineering teams are no longer the sole evaluator — data scientists and data-focused PMs now carry equal weight on the largest deals. The bar for statistical depth, warehouse ergonomics, and experiment-first workflows is rising quickly. In traditional experimentation we have built the foundation: a trusted runtime control plane, a growing experimentation engine, and early warehouse-native capabilities. We are winning lower-maturity buyers at healthy rates. We are not yet consistently winning the most sophisticated data organizations. Closing that gap is the job. In AI experimentation, we have an early lead: the AI-native tooling category has invested in evaluation and conceded production experimentation, and we already have the primitives (statistical significance, multi-armed bandits, experiment-aware guardrails) that no AI-native competitor ships. Extending that lead is the other half of the job. This leader will own whether LaunchDarkly becomes the definitive experimentation platform in an AI-accelerated world.

Responsibilities

Own the Experimentation pillar. Direct leadership of the Product team. Partner with Engineering and Design counterparts in a triad model. Accountable for the pillar's strategy, roadmap delivery, and commercial outcomes. Make the investment case across the in-product experimentation experience, the warehouse-native analysis layer, and the infrastructure that scales them.; Make experimentation the measurement layer of the AI SDLC. Partner with our AI product, observability, and core feature management leaders to productize the capabilities we already have as AI-native primitives. Build a closed loop from offline evaluation through production experiments, to automatic promotion and rollback, to a self-improving feedback loop for agents.; Win the high-maturity buyer. Earn the technical confidence of senior data scientists and data-focused PMs. Decide what statistical depth, warehouse coverage, and experiment-first workflow capabilities are non-negotiable, and get them shipped on a timeline that wins pivotal reference deals.; Make warehouse-native a weapon. Expand coverage across major data warehouses and query layers. Deliver parity on analysis-only mode, variance reduction, ratio and percentile metrics, exposure validation, and arbitrary-window analysis.; Operate a high-performing function. Run a disciplined roadmap, ship predictably against quarterly commitments, drive AI-assisted engineering productivity inside the org, and hire where gaps exist.; Be the external face of the category. Credibly represent the product with Data scientists, PMs, experimenters, analysts, and partners. Translate the strategy to the field and equip sales to win head-to-head.

Requirements

Senior product leader (GM, VP, or equivalent) with a track record of owning a product line that competes on statistical rigor and data infrastructure; Deep, operator-level fluency in experimentation methodology: causal inference, variance reduction, ratio metrics, sequential testing, exposure design, multi-armed bandits, and composite/multi-objective metrics — and the realities of running these at scale against production data warehouses and against non-deterministic systems where output variance, not just user variance, drives sample-size and significance decisions; Has earned credibility with data science leaders and experimentation specialists at sophisticated organizations — and can recruit them; Has led a function that includes engineering, design, and data science; Comfortable setting a multi-quarter roadmap, championing investment allocation, and reporting results to an executive team and board; Clear, direct communicator; Decides fast with incomplete information; Prefers shipping and learning to requirements documents; Opinionated about where experimentation is going in an AI-native world — and specifically, how agents and autonomous systems will use experimentation infrastructure differently than human teams do.

Nice to have

Built or scaled experimentation at an organization where it was core infrastructure, not a secondary analytics capability; Personally won competitive evaluations where a sophisticated data-science organization was the deciding voice; Shipped warehouse-native data products and understand the operational realities of running experiments directly against customer data infrastructure; Sees experimentation as how software teams prove that any change — whether built by a person or an AI agent — actually worked; Evidence layer is core infrastructure, not a reporting afterthought.

Benefits

20% bonus; Restricted Stock Units (RSUs); health insurance; vision insurance; dental insurance; mental health benefits