Senior Data Scientist - Marketing
$172,550–$203,000 year
RemoteUnited States
Job Summary
Develop models and optimization approaches to improve upper-funnel marketing performance, including spend allocation, channel mix, and budget pacing. Design decision systems that operationalize analytical outputs into business workflows, such as recommendation engines and scenario planning tools. Lead experimentation and causal measurement across marketing programs to distinguish signal from noise and inform investment decisions. Build trusted data foundations that unify marketing, revenue, and retail data to establish performance metrics. Create dashboards and reporting layers providing visibility into funnel health, campaign efficiency, and revenue contribution. Partner with stakeholders to translate ambiguous business questions into analytical frameworks and drive marketing spend decisions. Apply AI/ML methods, including LLM-enabled workflows, to improve decision speed and scale. Translate complex analytical work into clear recommendations for executive stakeholders to influence roadmap and growth strategy.
Required Qualifications
- 6+ years of experience in data science, machine learning, or advanced analytics, with a track record of owning high-impact business problems end to end
- Strong domain experience supporting Marketing, Growth, Retail, or other go-to-market functions in a consumer, e-commerce, retail, or digital business
- Hands-on experience building marketing data science solutions such as spend optimization models, channel or campaign measurement frameworks, media mix or incrementality approaches, propensity models, budget planning systems, or upper-funnel decisioning systems
- Demonstrated experience building not just models, but decision systems that operationalize analytical outputs into business workflows and recurring decisions
- Proven experience of data engineering intuition, including experience unifying and transforming data across multiple systems to create reliable, scalable datasets for downstream analysis, experimentation, and modeling
- Experience with experimentation, causal inference, forecasting, and optimization methods used to support business planning and growth investment decisions
- Strong proficiency in SQL (dbt) and Python, with experience building analytical data models, machine learning workflows, and production-quality measurement systems
- Experience applying AI techniques in practical business settings, ideally including LLM-enabled workflows, recommendation systems, decision support tools, or other AI-native products that augment human decision-making
- Experience working with modern cloud and data platforms such as Databricks, AWS, dbt, Snowflake, or similar tools
- Proven ability to communicate complex analytical concepts clearly to non-technical partners and to translate model outputs into recommendations the business can act on
- Proven ability to partner effectively with cross-functional and distributed teams in fast-moving environments
Desired Qualifications
- Experience in e-commerce, DTC, retail, or omnichannel growth environments
- Experience with marketing mix modeling, incrementality testing, attribution, or budget planning workflows
- Experience connecting marketing, retail, and commercial performance data to downstream revenue outcomes
- Experience in digital health, consumer technology, or wearable products
- Experience mentoring other data scientists and helping shape cross-functional analytical roadmaps
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