Growth Marketing Strategist Job Description Template

A Growth Marketing Strategist owns the systematic process of identifying, testing, and scaling the marketing levers that reduce customer acquisition cost, improve conversion rates, and increase revenue per customer. Unlike a channel specialist who optimizes within a single platform, a Growth Marketing Strategist operates across the full funnel — from top-of-funnel acquisition through activation, retention, and referral — treating every stage of the customer journey as an optimization opportunity with measurable experiments. They design test frameworks, analyze cohort data, build the measurement infrastructure to detect causal impact rather than correlation, and work with channel teams to implement winning experiments at scale.

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What Does Growth Marketing Strategist Do?

Companies hire a Growth Marketing Strategist when they have reached a stage where adding more spend to existing channels produces diminishing returns, and the strategic question is no longer "how do we execute better" but "which levers should we be pulling and in what sequence." This typically occurs post-product-market fit, when the company has enough conversion data to run statistically valid experiments, enough revenue to absorb the cost of losing test variations, and enough team infrastructure to implement changes quickly. A strong Growth Strategist brings a structured prioritization methodology (ICE scoring, PIE framework, or proprietary scoring), a deep understanding of statistical validity in marketing experimentation, and a track record of experiments that produced compounding rather than one-time gains.

Job Description Template

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Job Description · Ready to copyUpdated 2026

Job Title

Growth Marketing Strategist — [Full-Time / Part-Time / Contract] · Remote

About the Role

Key Responsibilities

Build and maintain a growth experimentation backlog: prioritizing test ideas using an ICE score (Impact, Confidence, Ease) framework, ensuring each experiment has a clearly defined hypothesis, primary metric, secondary guardrail metrics, minimum detectable effect, required sample size, and expected test duration before approval — preventing underpowered tests from producing false conclusions.
Design and run full-funnel conversion rate optimization (CRO) experiments: A/B tests on landing page headlines and hero section layout, pricing page structure and tier framing, checkout flow step count and form field reduction, onboarding sequence length and activation milestone placement — coordinating with design and development to implement test variants without polluting production data.
Own the customer acquisition cost (CAC) and LTV model: building cohort-level CAC by channel and campaign, calculating 30/60/90-day retention curves by acquisition source to determine true LTV contribution, and using CAC-to-LTV ratios by cohort to make channel budget allocation recommendations that maximize payback period rather than just conversion volume.
Conduct funnel drop-off analysis using GA4, Mixpanel, or Amplitude: identifying the highest-impact conversion gaps between funnel stages (e.g., 40% drop between landing page and sign-up form, 60% drop between free trial activation and first value milestone), quantifying the revenue impact of each drop-off, and building a prioritized remediation roadmap with estimated lift projections.
Develop and execute the referral and word-of-mouth strategy: designing referral incentive structures calibrated to LTV (reward high enough to motivate referral without inverting the unit economics), building the referral flow mechanics in a platform like ReferralHero or Viral Loops, and measuring referred-customer cohort quality against organic and paid cohorts.
Own the retention and churn reduction program: identifying churn leading indicators from product usage data, designing behavioral email and in-app trigger sequences that intervene before churn occurs, running win-back experiments for lapsed customers, and building the cohort retention dashboard that tracks monthly retention curves by acquisition source and pricing tier.
Coordinate growth experiments across channel teams: briefing the paid media team on acquisition experiment parameters, working with the email specialist on lifecycle sequence tests, partnering with the product team on onboarding and activation experiments — synthesizing results across channels into a unified growth model rather than siloed channel performance reports.
Build and maintain the growth reporting infrastructure: configuring GA4 event taxonomy for consistent funnel tracking, setting up Looker Studio dashboards with north-star metric trends (weekly active users, weekly revenue, activation rate, 30-day retention), and delivering a weekly written growth update to the leadership team with experiment results, metric variances, and recommended next actions.
Conduct competitive and market research to identify growth channels, positioning gaps, and conversion tactics that competitors are deploying: using tools like SimilarWeb for traffic source benchmarking, SpyFu for paid search competitive intelligence, and social listening tools to monitor competitor messaging shifts — translating findings into specific experiment ideas within 2 weeks of discovery.

Requirements

4+ years of growth marketing experience at a company with a functioning analytics infrastructure — candidates must demonstrate experience running statistically valid A/B tests, not just making optimization changes and observing whether metrics went up afterward. Provide a specific example of an experiment you designed, the result, and how you determined statistical significance.
Proficiency in at least one product analytics platform (Mixpanel, Amplitude, or GA4) beyond basic pageview tracking: building funnel reports, cohort retention analyses, user segmentation by behavioral property, and event-based conversion tracking that connects marketing touchpoints to downstream product activation and revenue events.
Statistical literacy sufficient for experimental design: understanding of statistical significance (p-value interpretation), minimum detectable effect calculation, sample size requirements for a given conversion rate and expected lift, and the practical implications of running multiple simultaneous tests without a holdout methodology.
Full-funnel conversion optimization experience: examples of experiments run at multiple funnel stages (landing page, onboarding, retention) with documented hypotheses, test setups, results, and learnings — not just top-of-funnel acquisition optimization.
CAC and LTV modeling experience: has built a cohort-level unit economics model that separates blended CAC into channel-level CAC, calculated LTV using retention curve data rather than assumed churn rates, and used the resulting payback period analysis to make a channel budget allocation recommendation that was implemented.
Cross-functional collaboration track record: has worked directly with product, engineering, and design teams to implement growth experiments — understands the developer handoff process, can write an experiment specification document that a developer can implement without ambiguity, and has navigated prioritization conflicts with engineering teams.
Growth channel breadth: has run experiments across at least three distinct channels (e.g., paid acquisition, SEO, email lifecycle, in-product onboarding, referral) — not a channel specialist who reframes their work as growth strategy without demonstrating genuine cross-channel scope.

Nice to Have

Experience with a product-led growth (PLG) motion: designing free trial activation sequences, building usage-based trigger logic for upgrade prompts, measuring time-to-value as a leading indicator of conversion, and running experiments to reduce the activation gap between signup and first meaningful product usage.
SQL proficiency for ad hoc data analysis: querying a data warehouse (BigQuery, Snowflake, or Redshift) to build cohort analyses, identify behavioral patterns before the BI team can build a formal dashboard, and validate that analytics platform data matches raw event data before drawing strategic conclusions.
Attribution modeling experience beyond last-click: has implemented or evaluated a data-driven attribution model, multi-touch attribution, or incrementality testing methodology to measure the true causal contribution of marketing channels — and can articulate the practical limitations of each approach for a given business model.
Experience at a company that scaled from $1M to $10M+ ARR — having lived through the specific growth challenges (PMF refinement, CAC inflation as initial audiences saturate, retention curve stabilization, team scaling) that most growth strategy frameworks describe but few practitioners have personally navigated.

Compensation

In-house Growth Marketing Strategists typically earn $80,000–$130,000/year ($38–$62/hr) in the US, with senior strategists at high-growth SaaS or DTC companies reaching $130,000–$175,000. Alternatively, hire a pre-vetted Growth Marketing Strategist through EverestX at $10–$12/hr full-time (approximately $1,700–$2,100/mo) — with a 48-hour match, replacement guarantee, and zero recruitment or platform fees.

Work Type

Remote · [Full-Time or Part-Time]

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Hiring FAQs

What is the difference between a Growth Marketing Strategist and a Performance Marketing Manager?+

A Performance Marketing Manager operates within paid channels — managing ad accounts, bid strategies, audience targeting, and creative testing to maximize ROAS from paid spend. A Growth Marketing Strategist operates across the entire funnel — acquisition, activation, retention, and referral — designing experiments that improve conversion rates at each stage and building the measurement framework to distinguish causal improvements from coincidental metric movements. The skills overlap in analytics and experimentation methodology, but the scope is fundamentally different. Performance marketing is a subset of growth marketing; a Growth Strategist should understand paid media well enough to evaluate it, but their primary leverage is cross-funnel conversion optimization, not in-platform bid management.

How do you know when a company is ready for a Growth Marketing Strategist?+

Three conditions must exist: enough conversion data to run statistically valid experiments (generally 1,000+ monthly conversions at the funnel stage you want to optimize), enough team infrastructure to implement test variants within 1–2 weeks of experiment design (otherwise the backlog stalls), and executive alignment that growth is a measurement and experimentation discipline rather than a channel spending exercise. Companies that lack these conditions typically benefit more from channel specialists who improve execution quality before a strategist who designs experiments against a thin data set.

Should the Growth Marketing Strategist own the marketing budget?+

A Growth Strategist should have meaningful input into budget allocation decisions — specifically the ability to recommend shifting budget from saturated channels to underexplored ones based on cohort LTV and payback period data. Direct budget ownership is more appropriate for a Head of Growth or CMO role. The most effective structure positions the Growth Strategist as the analytical owner of the growth model with clear escalation authority to the CMO or CEO for budget reallocation recommendations above a defined threshold. This gives the strategist the credibility to make data-driven allocation arguments without creating organizational friction by controlling budget decisions that span multiple channel teams.

How many experiments should a Growth Marketing Strategist run per month?+

Quality and cadence are both important: 4–8 experiments per month is a realistic throughput for a focused strategist working with a team that can implement changes in 1–2 week cycles. Fewer than 2 experiments per month indicates that implementation bottlenecks or approval processes are limiting the growth program's learning velocity. More than 12 simultaneous experiments creates interference problems — overlapping audience exposure contaminates results, and the team cannot implement and monitor too many concurrent tests without introducing errors. The compounding value of a growth program comes from the accumulation of validated learnings, not from maximizing test count at the expense of experimental rigor.