• Separate durable preference from noise. Design robust feature representations from high-cardinality, implicit behavioral data (swaps, skips, saves) to capture true user intent and predict future engagement. • Model temporal dynamics and changing tastes. Architect sequential and recency-aware systems that adapt to shifting user preferences, ensuring recommendations reflect current intent rather than stale history. • Solve the cold-start problem. Leverage cohort signals, clustering, and content embeddings to generalize learnings across users, ensuring that even a new customer’s first box feels deeply personalized. • Bridge ML and constrained optimization. Integrate model scores (e.g., predicted conversion) with operations-research engines to perform business-aware re-ranking, balancing personalization with hard constraints like diet, budget, and inventory. • Advance the modeling. Evolve our systems using the architectures that drive modern, high-scale personalization, such as multi-stage retrieval and ranking, learning-to-rank (LTR), matrix factorization, and gradient-boosted trees. You will also evaluate and integrate more sophisticated techniques (like contextual bandits or sequence modeling) as our data complexity grows. • Drive rigorous experimentation. Define robust offline evaluation metrics (e.g., NDCG, MAP) and design online A/B tests to measure true causal impact on customer retention and satisfaction

• Own the definition, consistency, and integrity of Oura’s core business metrics across product, finance, and go-to-market. • Establish and maintain a unified “metrics layer” that ensures a single source of truth for executive and operational decision-making. • Resolve ambiguity in metric definitions and drive alignment across senior stakeholders when competing interpretations exist. • Ensure critical KPIs are consistently defined, reproducible, and trusted across the organization. • Partner with the CFO’s office and FP&A to define, document, and deliver the financial KPIs that show up in Board and exec reporting including but not limited to (Revenue, Gross profit/margin, adjusted EBITDA, Net Income, Units Sold, Warranty Rate, Membership, Engagement, and Retention and the long tail of corporate metrics). • Establish each financial KPI as a governed, audit-grade data product — locked definitions, single owner, quality tests, full lineage to source systems, formal sign-off by key stakeholders, and a documented change-management process. • Treat key data pipelines and metrics as production-grade systems with defined reliability standards. • Establish monitoring, validation, and escalation processes for data issues affecting tier-1 business metrics. • Drive structured incident management processes for data breaks, metric regressions, and reporting inconsistencies. • Improve end-to-end data quality across systems of record, transformation layers, and reporting tools. • Ensure the analytics delivered in external audit cycles — documentation, walk auditors through lineage, and ensure SOX-aligned controls and segregation-of-duties practices are reflected in how financial metrics are produced. • Coordinate large-scale initiatives spanning Data Engineering, Analytics, Finance, GTM, Product, Operations, and Technology. • Manage complex dependencies across business and technical systems to ensure consistent data flow and logic. • Establish governance forums and decision frameworks that ensure clarity, accountability, and fast resolution of tradeoffs. • Translate fragmented systems into coherent operating models that support scalable decision-making. • Design how data is used to drive planning, forecasting, and resource allocation across the company. • Partner with Finance and BizOps to ensure planning systems are grounded in consistent, trusted data. • Build mechanisms that connect operational metrics to strategic decision-making and executive prioritization. • Enable faster, higher-confidence decisions by improving signal clarity and reducing ambiguity in core business data. • Translate complex technical and systems challenges into clear, decision-oriented narratives for executives. • Provide structured updates on data health, system integrity, and transformation progress. • Drive organizational adoption of standardized metrics, reporting systems, and data definitions. • Influence without authority across senior leaders to ensure alignment on the “one version of truth.”

• Serve as the primary point of contact for SCCAF-related coordination between clients, contractors, and Olsson's engineering teams. • Manage SCCAF project schedules and align study milestones with construction timelines. • Coordinate the development and delivery of SCCAF reports, settings, labels, RFIs, and submittals. • Monitor project progress, identify potential risks, and proactively resolve issues that could impact schedule or quality. • Lead SCCAF coordination meetings, document action items, and ensure timely follow-through from all stakeholders. • Facilitate resolution of technical and data-related challenges, including equipment information, field discrepancies, and design revisions. • Manage the SCCAF process from project kickoff through final deliverables while maintaining accurate documentation and study records. • Support overall project operations, including invoicing coordination, document control, deliverable tracking, and quality assurance processes.

• Bridge the gap between unstructured, real-world data, and AI models • Structure clinical datasets • Write reproducible code • Enforce incoming data quality control • Design data dictionaries and ontologies • Participate in technical conversations with external partners • Translate ambiguous source data into harmonized, AI-ready assets • Map diverse clinical data to industry-standard biomedical ontologies • Design, build, and maintain data dictionaries, schemas, and metadata models • Establish, automate, and enforce data quality control frameworks • Write production-grade Python code for data cleaning and harmonization • Audit data to find missing variables, anomalies, and hidden biases • Utilize clinical data progression metrics