• Design, develop, implement, monitor, and maintain valuations related machine learning models • Manage data analysis and modeling projects leveraging appropriate statistical, mathematical or machine learning tools to support business strategies and outcomes • Manage analytic solutions (data, tools reports, models) that advance and accelerate business partner agenda • Generate and synthesize customer and market insights from a wide variety of data systems • Prepare reports of analysis results for business partners and other non-technical audiences • Develop needed databases, data structures, compute patterns and operational processes to extract maximum value from existing and new data assets • Write "production worthy" code and perform code review and necessary testing to ensure that data products meet the highest quality and production standards • Identify opportunities to apply statistical methods to improve business performance, advance new strategies or to solve existing challenges • Work cross-functionally with peers in Technology, product, and business analysts to identify new attributes and applications • Create business intelligence, dashboards, and/or other advanced analytics metrics and reports to adequately communicate the business narrative and offer recommendations that are actionable and have material impact • Provide mentorship and guidance to junior level team members, modeling effective working relationships and solution-building that demonstrates product value.

Role Description We are looking for an experienced Senior Data Scientist to partner with our Membership organization to optimize Oura’s membership business. You will drive membership growth, retention, pricing, and engagement by building data products and models that directly inform membership strategies, offers, and lifecycle programs. Your work will help Oura better understand and optimize for our membership subscription that grows lifetime value, and scales with both enterprise and consumer demand. You will play a key role in shaping our data and AI strategy for membership analytics, collaborating with various teams across Marketing, Finance, and Product organizations. This role is a “full-stack” Data Scientist role, requiring a strong data engineering understanding initially — collaborating closely with engineers to unify subscription data from various business systems — to create an integrated, trustworthy membership data layer. Over time, the role will evolve toward more advanced analytics and ML modeling, such as retention & LTV modeling, and pricing optimizations. This is a remote US role with a strong preference for candidates based in the US Eastern Time Zone. What You Will Do - Build and maintain data pipelines integrating subscription and membership data from source systems to provide a complete view of the member lifecycle and value. - Ensure data quality, lineage, and governance standards for membership data systems. - Build predictive models to understand drivers of member retention, churn, reactivation, and LTV by segment, channel, and cohort. - Lead the design, implementation, and analysis of A/B tests and other experiments across pricing, packaging, and lifecycle communications to quantify impact and inform membership strategy and roadmap decisions. - Partner with Marketing, Finance, and Product teams to design and evaluate offers that improve acquisition efficiency and member retention, and to integrate these learnings into long-term strategic roadmaps. - Build models to optimize membership growth and retention, informing business decisions to strategically optimize financial decisions. - Leverage data engineering and analytical skills to transform data into actionable and reportable insights. - Develop dashboards and reporting tools to visualize membership funnel KPIs, cohort performance, and the impact of product/business changes on member outcomes. Qualifications - 6+ years of experience in data science or analytics, particularly at a subscription/membership business. A graduate degree in a relevant quantitative field is preferred. - Demonstrated success, in partnership with Membership/Subscription, Growth, or Marketing teams, applying data science and ML to optimize subscription funnels, pricing, offers, or lifecycle programs. - Experience with time series forecasting and survival analysis. - Strong SQL and data engineering skills, with experience unifying and transforming data from subscription management services/platforms, and integrating these datasets with other business and product data. - Strong proficiency in Python for building statistical, machine-learning, forecasting, and optimization models. - Experience with data visualization tools (e.g., Tableau, Looker, Power BI, etc) for executive-level financial reporting. - Experience with data development leveraging modern data stack and cloud platforms (e.g. AWS, Databricks, dbt, etc). - Proven ability to partner effectively with Marketing, Growth, and Engineering leaders, moving quickly to translate technical work into strategic insights/decisions. - Proven ability to translate complex analyses into clear, concise narratives and recommendations for senior executives, influencing roadmap and investment decisions. - Strong business acumen with deep understanding of membership metrics and decision-making frameworks. - Experience working with international and distributed teams. Benefits - Competitive salary and equity packages - Health, dental, vision insurance, and mental health resources - An Oura Ring of your 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. - Region 1: $172,550 - $203,000 - Region 2: $158,950 - $187,000 - Region 3: $147,900 - $174,000

• Building models for "what if" scenarios planning of the impact of market dynamics, regulatory changes, and pharmaceutical innovation on the financials of the company and our clients.

• Deliver automated document extraction and data enrichment solutions. • Manipulate data using Python or SQL Server; develop ad hoc queries to investigate data anomalies and to summarize data for pattern detection. • Manually label and validate small training data sets. • Develop round-trip data flows that prepare data for IDP and format the results for analytical purposes. • Collaborate with experienced modelers to build predictive models and analytic solutions using Python; apply techniques such as decision trees and clustering. • Under supervision use AI technologies such as natural language processing to extract business data from unstructured and semi-structured sources (e.g., loss history reports, insurance applications, claim files, application scraping using OCR/ generative AI, etc.). • Create technical documentation to archive end-to-end processes.