Head of Experimentation
LaunchDarklyFeature Management company
United States$256,000 - $414,000Director
Bessemer Venture Partners
Redpoint
Uncork Capital
Insight Partners
Lead Edge Capital
Threshold
Product
About the role
TL;DR
Lead the experimentation pillar, driving strategy, roadmap, and commercial outcomes for LaunchDarkly.
- •About the Job: 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.
- •Key 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.
- •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.
Required skills
Product ManagementA/B TestingSQLProduct StrategyRoadmappingFeature PrioritizationProduct AnalyticsCompetitive AnalysisProduct OperationsStrategic PlanningVision Setting
Nice-to-have skills
PythonJavaScriptAWSGoogle CloudAzureDockerKubernetesCI/CDAgileScrum
Domain expertise
developer-toolsaiproductivity
Benefits & perks
Equity, Health insurance, Vision insurance, Dental insurance, Mental health benefits, Bonus
Tech stack
PythonSQLJavaScriptAWSGoogle CloudAzureDockerKubernetesCI/CDGitJiraConfluenceNotionSlackA/B TestingProduct ManagementAgileScrumRoadmappingStakeholder ManagementPRDsOKRsDesign ThinkingWireframingPrototypingProgram ManagementProject ManagementCustomer SuccessCustomer OnboardingImplementation Management