Responsibilities

• Turn data into decisions. Own the analytics narrative for key client relationships — taking a business question, deciding what's actually worth analyzing, and delivering a clear recommendation a marketing leader can act on.
• Build measurement that maps to the business. Design and evolve frameworks that translate marketing activity into commercial outcomes — ROI, CAC/LTV, revenue lift, efficiency — and explain what they mean in plain language.
• Lead experimentation and read the results. Plan, interpret, and communicate incrementality work (lift tests, holdouts, geo/market tests) so stakeholders trust the takeaway, not just the methodology.
• Direct the analysis, not just run it. Use SQL, Python, and AI coding assistants as tools to interrogate first-party, third-party, and digital/media datasets quickly — then focus your energy on what the numbers say and why it matters.
• Set the standard for analytics storytelling. Clear narratives, smart visuals, confident recommendations — executive-ready, every time.
• Partner across the agency. Work with strategy, media, creative, and technology to embed analytics into planning and optimization cycles rather than reporting from the sidelines.
• Modernize how insight gets delivered. Reduce manual reporting, improve data trust, and put practical AI/ML to work in analytics workflows (automated QA, anomaly detection, assisted first drafts with human review).
• Raise the bar for the team. Mentor junior analysts on craft and communication, and help shape the templates, documentation, and best practices that let a growing team perform consistently.

Requirements

• 4–6 years in marketing analytics, growth analytics, or marketing science. Agency or client-facing experience strongly preferred.
• You make data make sense. You can take a messy, ambiguous business question, figure out what's worth answering, and tell the story back in language a CMO understands.
• Marketing brain first. You think in terms of marketing strategy and commercial outcomes, not just models and metrics.
• Client-facing presence. You're comfortable presenting to clients and stakeholders and can hold your own in the room with a clear point of view.
• Technically fluent — enough to direct the work. You have working knowledge of SQL and Python: enough to structure an analysis, query data directly or steer an AI coding assistant, and sanity-check what comes back.
• Comfortable with the modern stack. Hands-on with BI/visualization tools (Tableau, Power BI, Looker, or comparable) and at home in modern data environments, with exposure to cloud warehouses (BigQuery, Snowflake, Redshift) a plus.
• Pragmatic about AI. You see AI assistants as leverage for deeper, faster analysis — and you apply judgment about when to trust, verify, or set them aside.
• Steady in ambiguity. You balance near-term delivery with building practices that scale.

Preferred

• Experience with MMM, advanced attribution, or causal measurement in performance-driven environments.
• Familiarity with modern transformation practices (dbt), semantic/metrics layers, or data quality tooling — enough to collaborate, not necessarily to build.
• Experience folding consumer intelligence (survey, panel, CRM, demand data) into performance narratives.
• Comfort evaluating AI/ML approaches for marketing problems (propensity, LTV, churn, media response), even if you're not the one engineering them.
• This is a chance to do the kind of analytics work that actually moves client decisions — with room to grow your leadership, your craft, and your influence as the practice scales.

Benefits

• Medical, vision, and dental insurance
• Life insurance, Short-term and long-term disability insurance
• 401k with matching, FSAs, HSAs
• Flexible paid time off, plus major holidays, one floating holiday and volunteer time
• Paid parental leave
• And so much more!