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Oura is seeking a Senior Data Scientist to partner with Marketing, Finance, Retail, and Product teams to drive data-informed decision-making and business outcomes. This full-stack applied data science role combines strong data foundations, pragmatic model development, and the ability to translate technical work into operational and strategic action.
Key responsibilities include developing models and optimization approaches for upper-funnel marketing performance (spend allocation, channel mix optimization, budget pacing, media efficiency measurement, audience strategy, and revenue forecasting). You will design and operationalize decision systems that move the business from static reporting to repeatable, data-informed action, including recommendation engines, scenario planning tools, and AI-assisted workflows.
You will lead experimentation and causal measurement across marketing programs, build trusted data foundations unifying marketing, revenue, retail, and product data across channels, and create dashboards and analytical products that give leaders visibility into funnel health and campaign efficiency. The role requires partnering with stakeholders to define high-leverage growth and efficiency problems, translating ambiguous business questions into analytical frameworks, and driving marketing spend decisions through rigorous data science.
You will collaborate with Data Engineering to improve data quality, instrumentation, and pipeline reliability. The position emphasizes applying AI/ML methods where they create practical leverage—including predictive modeling, optimization, and LLM-enabled workflows—and translating complex analytical work into clear recommendations for non-technical and executive stakeholders. You will help raise the technical bar through strong project ownership, documentation, peer review, and mentoring.
Required qualifications: 6+ years 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 go-to-market functions in consumer, e-commerce, retail, or digital businesses. Hands-on experience building marketing data science solutions (spend optimization, channel/campaign measurement, media mix, incrementality, propensity models, budget planning, or decisioning systems). Demonstrated experience building decision systems that operationalize analytical outputs into business workflows. Data engineering intuition with experience unifying and transforming data across multiple systems. Experience with experimentation, causal inference, forecasting, and optimization methods. Strong SQL (dbt) and Python proficiency. Experience applying AI techniques in practical settings, ideally including LLM-enabled workflows and decision support tools. Familiarity with modern cloud and data platforms (Databricks, AWS, dbt, Snowflake, or similar).
