Growth Experimentation Manager

Seeking.com is the world’s largest premium dating platform, and we are entering the most important chapter of our brand transformation. We are looking for a Growth Experimentation Manager who will own the full experimentation program end-to-end as a hands-on operator. You will design the test, build the audience, ship the variant, read the result, and write the recommendation before the next standup.

This role sits at the intersection of marketing, product, and data. You will set the testing roadmap, run rigorous experiments across paid media, lifecycle, landing pages, onboarding, and creative, and translate findings into concrete channel and creative decisions. You will report to the Director of Growth & Lifecycle Marketing and partner closely with Data & Analytics, AI, Product, Engineering, Creative, and Brand.

We are not looking for a strategist who briefs the work to an agency or analyst. We are looking for a builder who can sit down in week one, audit what is being tested, identify what is broken or misdesigned, and ship a better-instrumented experiment by Friday. If you want a corporate seat where the analysis gets handed to someone else, this is not your role.

Seeking is moving past its legacy reputation and becoming the definitive platform for ambitious, eligible people building extraordinary lives. The fastest way to compound that transformation is to test our way to it: every channel decision, every creative call, every onboarding tweak should be informed by a clean, well-designed experiment rather than the loudest opinion in the room.

Tests run today, but they run inconsistently. Some lack proper control groups. Some are called too early. Some are statistically significant but business-irrelevant. Some never get documented, so we relearn the same lesson next quarter. We are not asking you to invent experimentation at Seeking from a clean sheet. We are asking you to bring rigor, velocity, and a single source of truth to a program that is already in motion, and then compound from there.

Experimentation is the connective tissue between every growth function. The Growth Experimentation Manager is the person who makes sure the right tests run, the data is trustworthy, and the learnings translate into action across paid, lifecycle, web, and creative.

Experimentation Strategy & Roadmap

• Build, maintain, and prioritize a comprehensive testing roadmap across paid media, email/lifecycle, SEO, landing pages, onboarding, paywall, and creative.
• Develop a hypothesis backlog informed by quantitative funnel analysis, qualitative user insights, competitive research, and channel team input.
• Define and enforce a structured prioritization framework (ICE, PIE, or a custom variant) to stack-rank tests by potential impact, confidence, and ease of execution.
• Establish testing velocity benchmarks and ensure the team runs the optimal number of concurrent, non-interfering experiments at all times.
• Maintain an always-current view of what is being tested, what has been learned, and what is next, and communicate it weekly to leadership and channel owners.

Test Design & Statistical Rigor

• Design A/B, multivariate, holdout, and geo-based experiments with proper control groups, adequate sample sizes, and pre-defined success metrics.
• Partner with Data & Analytics and Engineering to ensure correct instrumentation, event tracking, and attribution before any test launches.
• Define primary, secondary, and guardrail metrics for every experiment to capture both intended lift and unintended side effects.
• Apply the right statistical method for the test type, traffic volume, and decision urgency, and know when to call a test early vs. wait for significance.
• Identify and mitigate threats to validity: novelty effects, seasonality, sample ratio mismatch, network effects, and interaction effects between simultaneous tests.

Analysis & Results Interpretation

• Conduct deep post-experiment analysis going beyond top-line win/loss to understand segment-level effects, interaction effects, and downstream impact on LTV.
• Distinguish between statistical significance and business significance. Never let a technically significant result drive a bad business decision.
• Build and maintain a results repository capturing test parameters, outcomes, learnings, and confidence levels, making institutional knowledge searchable and actionable.
• Present findings to cross-functional stakeholders in a clear, non-technical narrative that connects test outcomes to business strategy.

Recommendations & Next-Step Programming

• For every completed experiment, deliver a structured recommendation by a committed date: ship it, iterate on it, or kill it, with supporting rationale and proposed next steps.
• Translate winning test results into channel-specific updates: campaign and targeting changes for Paid Media; flow logic and segmentation for Lifecycle/CRM; landing page and onboarding updates with Product; creative direction briefs for the Creative team; SEO and UX recommendations from engagement tests.
• Flag when a result should trigger a broader strategic shift vs. a tactical tweak, and escalate those moments proactively.
• Build iterative test sequences where each experiment compounds prior learnings, rather than running isolated one-off tests.

Cross-Channel Insight Synthesis

• Connect the dots across channels. Surface patterns from paid media tests that should inform lifecycle messaging, and vice versa.
• Build shared creative and messaging frameworks derived from test learnings that every channel owner can apply.
• Partner with the Lifecycle Marketing & CRM Manager, the SEO Manager, and the Performance Media Manager to ensure experimentation coverage across organic, paid, and owned funnels.
• Serve as the primary liaison between Marketing and Data & Analytics for testing, translating business questions into test designs and analytical outputs back into marketing action.

Experimentation Infrastructure & Culture

• Evaluate, implement, and manage experimentation tooling: A/B testing platforms, feature flagging, geo-experiment tools, and analytics integrations.
• Define and document testing standards, naming conventions, QA checklists, and launch/kill criteria so experiments are run consistently across the team.
• Champion a test-and-learn culture. Run workshops, share weekly wins and learnings, and build org-wide confidence in data-driven decision making.
• Identify gaps in tracking, attribution, or event reliability that limit clean experimentation, and advocate for fixes with Engineering and Data.

• 3 to 5+ years of hands-on experimentation, CRO, growth marketing, or analytical marketing experience, preferably at a consumer subscription, marketplace, mobile-first, or dating/social platform. The right wiring matters more than the years.
• Ready to run on day one. You can audit an existing test program, identify what is misdesigned, and ship a better-instrumented experiment in week one.
• Deep understanding of experimental design: control/treatment setup, sample sizing, statistical significance, power calculations, novelty/seasonality/SRM threats, and how to mitigate them.
• Hands-on, in-the-tool expertise with at least one experimentation or A/B testing platform (Optimizely, VWO, Statsig, LaunchDarkly, Eppo, Convert, AB Tasty, or comparable). You build the tests yourself.
• Strong analytical fluency. Comfortable in SQL and directly in the warehouse, plus a product analytics tool (Amplitude, Mixpanel, Looker, Heap, or similar) to pull your own analysis and pressure-test Data team outputs.
• Proven track record of running experiments across at least two of: paid media, email/lifecycle, landing pages, onboarding, paywall, or in-product marketing, with numbers you can speak to.
• Outstanding communication and storytelling. You can write a crisp experiment brief and present nuanced results to a non-technical audience with equal confidence.
• Experience with holdout group design and geo-based experiments, and a clear point of view on when each is the right tool.
• AI fluency. You already use ChatGPT, Claude, Cursor, Cowork, or similar in your daily workflow to draft test plans, generate variant copy and creative, analyze results, write SQL, and replace work you used to do by hand. You can describe the AI workflows you have built for yourself. 
• Operator’s discipline

• Experience at a consumer subscription, marketplace, or dating/social platform where engagement and retention are primary growth levers.
• Familiarity with Bayesian experimentation methods and a clear view on when to apply them vs. frequentist approaches.
• Background in behavioral economics or consumer psychology. Understanding why people behave the way they do makes for sharper hypotheses.
• Experience building or contributing to a company-wide experimentation program from the ground up, including governance, tooling selection, and team training.
• Exposure to multi-armed bandit testing, contextual bandits, or adaptive experimentation.
• Experience designing experiments around AI-driven personalization or recommendation systems.
• Bachelor’s degree in Statistics, Computer Science, Marketing, or a related field, or an equivalent track record of rigorous experimentation

• Base salary: $110,000 to $145,000, commensurate with experience.
• Full-time, exempt, fully remote within the US (with travel for meetings) or Hybrid in Las Vegas, NV.
• Health, dental, vision, 401(k), and a standard benefits package.
• Direct exposure to the Director and Co-CEOs from day one.
• Clear growth path to Senior Growth Experimentation Lead within 12 to 18 months, based on outcomes.

Seeking.com is an equal opportunity employer. We make hiring decisions based on capability and fit.

This job description is intended to convey essential responsibilities and is not exhaustive. Duties may evolve as the business grows.