Marketing Analyst at Headspace

About the Marketing Analyst at Headspace:

Headspace is on a mission to improve the health and happiness of the world. Our marketing team drives growth across both direct-to-consumer subscriptions and B2B partnerships with employers, health plans, and strategic partners. Behind that growth is a real measurement challenge: multiple acquisition channels, a mobile-first attribution landscape, a rich lifecycle program, and partners who each need their own view of the numbers.

The Marketing Analyst is the vital analytics partner who enables the marketing org to make data-driven decisions and navigate complexity with clarity. You will own the measurement, attribution, and experimentation work that tells us what is working, why, and what to do next. This is a proactive, impactful role—we are seeking a dedicated thought partner who brings unique insights and thrives on driving initiatives forward.

What you will do:

Measurement & reporting

• Build and extend the marketing metrics (signups, cost per signup, CPA, attribution, engagement, retention) and reporting so partners can self-serve and trust the numbers, rather than rebuilding the stack from first principles each time.
• Design the measurement approach alongside a campaign — defining KPIs, instrumentation needs, and success criteria before launch, so the team leads with data instead of reverse-engineering it afterward.
• Own a process that connects campaign activity to downstream business signals (engagement spikes, care utilization, conversions) in near-real-time — replacing recurring fire drills with reliable, proactive reporting.

Experimentation & causal analysis

• Partner on experiment design: pressure-test the hypothesis, check that tests are powered and correctly set up, and be clear about what we are actually trying to learn.
• Run post-mortems that go past “what happened” to “what we should do next,” proposing follow-on iterations and optimization plays.
• Apply causal methods — incrementality testing, geo/holdout designs, and media mix modeling — to separate true signal from noise.

Acquisition & paid media analytics

• Build LTV analysis by channel to guide budget allocation and channel strategy.
• Stand up and maintain attribution across SKAN (iOS) and web, and reconcile platform-reported, modeled (MMM), and self-reported views into a coherent picture.
• Partner with Product to land a “How did you hear about us” (HDYHAU) survey in the mobile onboarding flow, then turn it into a usable self-reported attribution signal.

Lifecycle & retention analytics

• Analyze trial-to-paid conversion, engagement, churn, and reactivation across the lifecycle — without needing a briefing on what those metrics mean.
• Work fluently with CRM / lifecycle data structures (e.g., Braze) and journey/funnel analysis.

What you will bring:

Required Skills:

• 4+ years in marketing, growth, or product analytics, or a comparable quantitative role.
• Strong Tableau and SQL skills, and comfort working directly in a data warehouse.
• Hands-on fluency with a product analytics platform (Amplitude or similar) and with marketing measurement generally.
• Marketing-metrics literacy — open and click rates, conversion funnels, CAC/CPA, LTV, and attribution concepts.
• Demonstrated experimentation and causal-inference skills: A/B testing, power analysis, incrementality, and holdout design.
• A track record of self-directed ownership — you drive your own deadlines and flag slippage proactively, before anyone has to chase.
• The ability to translate analysis into clear, actionable recommendations for non-technical partners.
• Care and rigor when handling sensitive member and health-related data.

Preferred Skills:

• Mobile attribution experience (SKAN / SKAdNetwork) and familiarity with media mix modeling tools (e.g., Recast).
• Experience with Braze or a comparable CRM / lifecycle marketing platform.
• Experience supporting B2B marketing and partnership analytics, including account- or org-level reporting.
• Background in subscription, consumer app, or digital health businesses.
• A working relationship with, or strong instinct for partnering with, Data Engineering.