Growth Experimentation Manager

Growth Experimentation Manager

Growth & Lifecycle Marketing | Full-Time | Reports to Director of Growth & Lifecycle Marketing

About the Role

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 statistics, 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.

Let’s be direct: 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 that is you, keep reading. If you want a corporate seat where the analysis gets handed to someone else, this is not your role.

Why This Role Exists

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.

Here is the honest situation: 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.

Who Should Apply

We care more about wiring than resume. We are especially open to candidates from these backgrounds:

• Hands-on growth experimentation or CRO ICs at consumer subscription businesses (dating, streaming, fitness, fintech, news) who have personally designed and shipped the experiments, not just briefed them. Some of you may carry a Senior Manager title today; we care about how close you stay to the work.
• Senior Growth Experimentation Specialists, Analysts, or Senior Analysts who are top performers in their current seat and ready for the step up. If you have been the person actually designing the tests, writing the SQL, and reading the results while someone else owns the roadmap, this is the role where you own the whole thing. Title at your current company is not the bar; demonstrated wiring is.
• Growth marketers with deep analytical chops who can write the SQL, design the test, ship the variant, and read the result without a translator.
• Experimentation or CRO leads from two-sided marketplaces or mobile-first products who already think in terms of supply, demand, and matching, not just conversion rate.
• Data scientists or analysts who have moved into growth and prefer driving decisions to writing dashboards.
• CRO operators from a top consumer agency who are ready to own one brand, full stack, end to end.

If you brief the experiment to a vendor or hand the analysis to a data team and wait, this role is not for you. On day one, you should be able to log into the testing platform, audit what is running, identify what is misdesigned, write a sound new test plan, and ship it. That is the bar.

What You’ll Find on Day One

• A live testing program across paid media, lifecycle, landing pages, onboarding, and creative, with members actively in test cells today.
• Inconsistent rigor. Some tests have clean control groups and clear success criteria. Many do not. Several need to be re-designed, re-instrumented, or stopped.
• Real data and real revenue. A data warehouse, a behavioral event stream, an MAP and an experimentation platform you can use to actually move numbers.
• A Director who wants you running, not ramping. You will get context, access, and air cover. You will not get a six-month onboarding plan.
• Permission to ship in week one. Your first quick win is expected, not a stretch. Audit the active tests, kill what is invalid, and put one well-designed experiment live.

The Experimentation Lifecycle You’ll Own

Every test you ship moves through the same six-step lifecycle. You own it end-to-end, and you raise the bar on each step.

What You’ll Own

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.

Required Qualifications

• 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. If someone has to explain to you why AI matters, this role is not for you.
• Operator’s discipline. You ship on time, your QA is tight, nothing leaks between channels, and you document every result.

Preferred Qualifications

• 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, Economics, Mathematics, Computer Science, Marketing, or a related field, or an equivalent track record of shipping rigorous experiments.

Critical Success Factors

• Builder, not briefer. You design the test yourself, write your own SQL, and read your own result. You do not need a vendor or analyst to translate.
• Statistical conscience. You hold the line on rigor when the business is asking you to call a test early or interpret a flat result as a win.
• Business judgment. You know that a 2% statistically significant lift on a tiny segment is not the same as a real business win, and you can explain the difference to a CMO.
• Channel coherence. You design tests that respect what other channels are doing. You do not run two experiments that contaminate each other.
• AI as leverage. You treat AI as a force multiplier and have already replaced parts of your old workflow with it.
• Operator’s discipline. Documentation, QA, and instrumentation are not someone else’s job. They are yours.
• Brand guardian. Every variant that touches a member meets the bar of the Seeking brand, not just the bar of the test.

Cultural Fit: Who Thrives Here

Be honest with yourself about whether this is your environment:

• You move fast. You would rather ship a well-designed test this week than the perfect test next quarter, then iterate.
• You take ownership. When you see a broken event, a contaminated cell, or a misread result, you fix it. You do not wait to be told.
• You are a builder. You like the doing as much as the planning. You do not consider operational work beneath you.
• You are intellectually curious. You read papers, you sign up for betas, you have opinions on Bayesian vs. frequentist before anyone asks.
• You believe in the brand. You can advocate for Seeking with conviction, not detachment, and you care about helping ambitious people find genuine connection.
• You have a high bar. You hold yourself accountable to numbers and to craft, and you expect the same of the people around you.
• You sell on opportunity, not comforts. You are here to build the best experimentation program in the dating category, not to clock hours.

Day in the Life

• Morning review of in-flight tests: traffic, sample ratio, guardrail metrics, and any anomalies from overnight.
• Stand-up with the Director, a 15-minute check on priorities and blockers.
• Design a new test in the experimentation platform: hypothesis, audience, variants, success metrics, sample size, and pre-registered analysis plan.
• Pull a SQL query to validate a segment definition before the test goes live, and pressure-test the Data team’s LTV model with your own cut.
• QA a paid media test with the Performance Media Manager. Make sure the holdout is clean and the events fire correctly.
• Write up last week’s landing page test result: top-line lift, segment cuts, downstream LTV signal, and a ship/iterate/kill recommendation.
• Run a 30-minute workshop with the Creative team on what the latest creative tests have taught us about resonance.
• Sync with the Lifecycle Marketing & CRM Manager to make sure your paywall test and her renewal test are not interfering.
• Use Claude or Cursor to generate variant copy, draft a results memo, or scaffold a SQL query, then make the editorial and analytical calls yourself.
• End-of-day: update the experimentation roadmap, queue tomorrow’s launches, and document one new learning in the results repository.

How We Will Measure Success in the First 12 Months

• 30 days. Audit of in-flight tests complete. Invalid tests stopped or re-designed. Stakeholder interviews done. Draft experimentation charter and prioritization framework presented to the Director.
• 90 days. First owned experiments live across at least two channels. Testing calendar and weekly results cadence adopted by the team. Results repository in use.
• 6 months. Measurable funnel improvements attributable to experiments. Institutional knowledge base of test results actively referenced in roadmap and channel decisions. Testing velocity benchmarks documented.
• 12 months. Experimentation recognized as a core growth lever across the company. Documented playbook for channel, creative, lifecycle, and paywall testing that scales with team growth. A clear path established for the next experimentation hire.

Why This Role Matters

Dating platforms live and die on activation, engagement, and retention. In a category where small lifts compound into meaningful LTV gains, the quality of our testing program is the quality of our growth. The Growth Experimentation Manager is the person who decides which questions we answer, how rigorously we answer them, and how quickly we act on the answers.

This is also a launchpad. As the experimentation function matures, the right person in this seat has a clear path to Senior Growth Experimentation Lead within 12 to 18 months, judged on outcomes, not tenure.

Compensation & Benefits

• Base salary: $110,000 to $145,000, commensurate with experience.
• Full-time, exempt, fully remote within the US. Strong preference for candidates open to relocating to Las Vegas, NV.
• Health, dental, vision, 401(k), and a standard benefits package.
• Direct exposure to the Director, CMO, 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.

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