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.
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.
How you'll be measured:- Win rate on experimentation-involved deals, especially head-to-head competitive evaluations - step change in the first year, sustained improvement thereafter.
- Reference-grade customers at the top of the maturity curve, including named strategic logos.
- Monthly active customers and active-account ARR growth against plan.
- Experimentation attach rate on new and expansion enterprise deals.
- Engineering throughput - roadmap delivery velocity and AI-assisted development adoption inside the function.
Qualifications:- 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.
Preferred Qualifications: - You have built or scaled experimentation at an organization where it was core infrastructure, not a secondary analytics capability.
- You have personally won competitive evaluations where a sophisticated data-science organization was the deciding voice.
- You have shipped warehouse-native data products and understand the operational realities of running experiments directly against customer data infrastructure.
- You see experimentation as how software teams prove that any change - whether built by a person or an AI agent - actually worked. That evidence layer is core infrastructure, not a reporting afterthought.
Pay:Target pay ranges based on Geographic Zones* for Level M5:
- Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle - $301,000 - $414,000**
- Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago - $271,000 - $373,000**
- Zone 3: All other US locations - $256,000 - $352,000**
- All Zones inclusive of 20% Bonus
LaunchDarkly operates from a place of high trust and transparency; we are happy to state the pay range for our open roles to best align with your needs. Exact compensation may vary based on skills, experience, and location.
*Within the United States, our geographic pay zones are defined by counties surrounding major metropolitan areas.
**Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary.
