• 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.

• Define and execute Digital Science's researcher growth strategy, ensuring the channel delivers measurable contribution to company and segment growth objectives. • Build the programs and operating models that convert researcher engagement into commercial outcomes — from first adoption through retention and cross-product expansion. • Partner with Product, Marketing, and segment leaders (Academic, Enterprise, Government, Publisher) to prioritize investments and align initiatives with market priorities. • Build and scale a global researcher community — including ambassador and advocacy programs — that drives awareness, engagement, and commercial growth. • Develop and optimize direct-to-researcher commercial motions that convert engagement into revenue, pipeline, institutional demand, and customer expansion. • Monitor channel performance against targets, flagging risks early and acting decisively. • Build alignment across market, product, marketing, and commercial leaders, establishing shared goals and operating rhythms for coordinated execution. • Define the metrics that quantify the channel's contribution — revenue, pipeline, conversion, retention, lifetime value, cross-product adoption, and community engagement — and drive continuous improvement through experimentation and analysis. • As a member of the Commercial Leadership Team, embed researcher engagement into portfolio planning and product investment decisions, serving as the executive voice for researcher growth at the highest levels of the company.