Senior Data Scientist - Marketing

Responsibilities

• Develop models and optimization approaches that improve upper-funnel marketing performance, such as spend allocation, channel mix optimization, budget pacing, media efficiency measurement, audience strategy, and forecasted revenue impact.
• Design and operationalize decision systems that help the business move from static reporting to repeatable, data-informed action, including recommendation engines, scenario planning tools, and AI-assisted workflows.
• Lead experimentation and causal measurement across upper-funnel marketing programs, helping teams distinguish signal from noise and make better investment decisions across channels, campaigns, and retail partnerships.
• Build and maintain trusted data foundations that unify marketing, revenue, retail, and product data across channels and systems to establish performance measurement metrics.
• Build dashboards, reporting layers, and recurring analytical products that give leaders clear visibility into awareness and acquisition funnel health, campaign efficiency, retail performance, and revenue contribution.
• Partner with Marketing and other stakeholders to define the highest-leverage growth and efficiency problems, translate ambiguous business questions into analytical frameworks, and drive marketing spend decisions through rigorous data science.
• Collaborate with Data Engineering and business partners to improve data quality, instrumentation, metric definitions, and pipeline reliability across the marketing data ecosystem.
• Apply AI/ML methods where they create practical leverage, including predictive modeling, optimization, and LLM-enabled workflows that improve decision speed, quality, or scale.
• Translate complex analytical work into clear recommendations for non-technical and executive stakeholders, influencing roadmap, budget, and growth strategy decisions.
• Help raise the technical bar for the team through strong project ownership, documentation, peer review, mentoring, and thoughtful cross-functional collaboration.

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