• Lead the analytical development and quality control strategy for one or more programs, setting the technical direction for method development, validation, and lifecycle management to ensure robust, phase-appropriate control of product quality. • Partner with CDMO and contract laboratory partners as the primary technical point of contact, driving resolution of complex analytical or method-related issues and holding partners accountable to scientific and quality standards. • Lead drug substance (API) analytical method development, qualification, and validation, including forced degradation, related substances/impurity profiling, and physicochemical characterization to support process understanding and specification setting. • Perform in-depth review and interpretation of analytical data (HPLC/UPLC, Dissolution, KF, GC, etc) across release and stability programs, and personally lead the resolution of the most complex or ambiguous data issues. • Identify subtle trends, shifts, or outliers in complex data sets that may impact product quality, method robustness, or long-term stability, and translate findings into program-level risk assessments and recommendations for leadership. • Serve as the technical lead and final scientific authority during OOS, OOT, and atypical result investigations, driving root cause determination and ensuring scientifically sound, defensible conclusions. • Own preparation and technical accuracy of the analytical development and quality control data packages supporting regulatory submissions, and author the CMC analytical sections of regulatory documents (e.g., NADA/INAD filings, briefing documents, and responses to health authority questions), ensuring data and method documentation are complete, scientifically sound, and submission-ready. • Mentor and provide technical guidance to junior scientists and analysts, building analytical capability across the team.

• Leading end-to-end research across the product lifecycle from early discovery through post-launch evaluation • Designing and conducting qualitative and quantitative research using interviews, usability studies, surveys, and analytics • Partnering closely with cross-functional teams to influence product roadmap decisions and improve customer outcomes • Researching highly technical workflows involving content delivery, cloud infrastructure, automation, and edge computing • Building relationships with developers, engineers, and architects to deeply understand how they operate internet applications • Translating research findings into compelling insights, journeys, and recommendations that drive strategic product decisions

• Lead research and development of individual- and population-level health trajectory models that predict future member states, risks, and likely progression paths using messy, real-world longitudinal healthcare data. • Produce high-quality experimental evidence and technical recommendations that can lead to tangible product features and have a real impact on individual and population health trajectories. • Lead the design of next-best-action algorithms that convert predicted trajectories into intervention decisions tailored to a member’s current context and likely future path. • Research and apply advanced decision and recommendation policies to safely optimize intervention choice in a healthcare environment. • Define objective functions, reward signals, and policy constraints that balance engagement, clinical effectiveness, fairness, and operational feasibility, partnering with product and clinical teams to ensure outputs are actionable and interpretable. • Serve as the senior scientific lead for algorithmic and evaluation rigor in trajectories and next-best-action, setting standards for problem formulation, evaluation, and publication-quality analysis. • Mentor other scientists and data scientists on advanced methods in temporal modeling, reinforcement learning, and causal inference. • Collaborate closely with platform, MLOps, and product engineering teams to ensure research outputs can be productionized reliably and monitored appropriately.

• Own and pursue a research agenda focused on LLM evaluation, model quality, routing optimization, and AI usage patterns, contributing original insights that advance the field. • Design novel evaluation frameworks and benchmarks that go beyond standard leaderboards, using real-world generation data to capture how models actually perform across tasks and contexts. • Conduct large-scale empirical studies on LLM behavior: how models compare across providers, how performance changes over time, and how usage patterns reveal strengths and weaknesses. • Develop the statistical and mathematical foundations behind our routing systems, building the models and heuristics that power intelligent provider and model selection. • Identify opportunities to apply research findings to feed back into OpenRouter's product and platform. • Collaborate with external researchers, model providers, and the open-source community to advance shared understanding of LLM capabilities and limitations. • Work with product and engineering teams to translate research findings into improvements to OpenRouter's platform, without being constrained to a shipping cadence.