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.

Requirements

• 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.

Preferred

• 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.

Benefits

• Competitive salary and equity packages
• Health, dental, vision insurance, and mental health resources
• An Oura Ring of their own plus employee discounts for friends & family
• 20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
• Paid sick leave and parental leave
• Oura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates' pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions. These ranges may be modified in the future.
• Region 1 $172,550 - $203,000
• Region 2 $158,950 - $187,000
• Region 3 $147,900 - $174,000