Own the end-to-end marketing measurement strategy across experimentation, media mix modeling, and attribution for paid and PLG channels
Establish and evolve the attribution framework: how platform data, multi-touch attribution, MMM, and experiments work together to inform decisions
Translate complex measurement outputs into clear recommendations on where to invest, what to cut, and how to hit pipeline, revenue, and efficiency targets (CAC, payback, LTV to CAC)
Serve as the subject matter expert for marketing measurement, educating stakeholders on causality, model uncertainty, and the limitations of platform-reported metrics
Build and maintain media mix models using historical data to estimate channel contribution, marginal returns, and optimal budget allocation
Incorporate seasonality, adstock, and saturation effects, and continuously validate model performance through backtesting and reconciliation with experiment results
Turn MMM insights into budget scenarios and forecasts across channels and regions, communicated in a way non-technical stakeholders can act on
Own and build the context and skills that let marketing teams run accurate self-serve analytics: metric definitions, model documentation, and reusable analysis patterns, not just dashboards
Use AI tools as a core part of daily work to accelerate analysis and go deeper than a traditional analyst workflow allows, and help establish AI-forward practices across the marketing org
Identify gaps and inconsistencies in marketing data (tracking, spend, platform exports) and work cross-functionally to fix them at the root rather than patching around them downstream
Have 6+ years in marketing analytics, data science, or a related role, with a focus on performance marketing and/or PLG growth
Deeply understand marketing attribution, incrementality testing, and media mix modeling, and how they complement each other rather than compete
Are advanced in SQL and at least one statistical programming language (Python or R) for experiment analysis and modeling
Have hands-on experience designing, running, and interpreting experiments across digital marketing channels: search, social, display, email, in-product
Have built or worked closely with MMM and/or advanced attribution models, ideally in a consumption or BaaS environment
Have strong business acumen and fluency in growth metrics: CAC, LTV, payback period, conversion rates, funnel performance
Think in terms of self-service and scale: your instinct is to build tools and frameworks that make marketing teams independently capable, not to become the bottleneck for every "did this work" question
Can hold technical and strategic context at once: you're as comfortable in a model's residuals as you are in a conversation about budget tradeoffs
Are AI-forward in how you work: you use LLMs and AI tooling habitually and have a clear point of view on how it changes what an analyst can do
Communicate clearly to non-technical stakeholders and know how to make causal nuance land in a business conversation
Thrive in async, autonomous environments and are energized by building a measurement function from the ground up
Professional Development
Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth