• Define and own the KPIs that measure CX health, downstream impact, product experience, and user sentiment. • Analyze user behavior and marketplace dynamics to identify opportunities and inform CX product and agent team priorities. • Measure the tradeoffs between user experience and business value. • Translate complex data into actionable recommendations for product and leadership teams. • Partner with product managers and engineers to design, implement, and evaluate A/B tests and feature rollouts. • Develop frameworks for causal inference, impact measurement, and long-term ecosystem health. • Build scalable methodologies to understand feature performance and guide iteration. • Use our modern data stack to build dashboards, data pipelines, and self-serve tools that empower teams across Whatnot. • Partner with engineers and operations leadership to improve data accessibility, ensure data quality, and support instrumentation for new product features. • Advocate for data-driven decision-making and foster a culture of measurement across the CX organization. • Communicate insights clearly to both technical and non-technical audiences, influencing roadmaps and strategic decisions. • Serve as a thought partner to CX leads, shaping how we build, launch, and iterate on experiences across the platform.

• Design and implement scalable and reliable approaches to support or automate decision making throughout the business • Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear • Acquire data by building the necessary SQL / ETL queries and import processes through various company specific interfaces for accessing S3, RedShift, and Spark storage systems • Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies • Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks • Validate models against alternative approaches, expected and observed outcomes, and other business defined key performance indicators • Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production • Implement and deploy state of the art machine learning algorithms under Gen AI, build prototypes, troubleshoot customer issues, and explore new solutions • Interact closely with customers and with the academic community to drive innovation and deliver tailored data science solutions

• Collaborate in the development and application of advanced AI/ML methods, including cutting-edge computer vision techniques applied to ophthalmic imaging data (e.g., Optical Coherence Tomography and fundus images), to uncover disease mechanisms and identify novel biomarkers. • Collaborate in the development and validation of novel digital endpoints. • Engage with regulatory stakeholders to ensure these innovations enhance clinical trial design, improve patient monitoring and care pathways, and meet regulatory requirements. • Develop and apply sophisticated statistical models using real-world and clinical data to generate insights into disease progression, treatment outcomes, and patient stratification. • Leverage longitudinal disease modeling, Bayesian methodologies, and causal inference techniques to inform decision-making. • Apply emerging generative AI approaches to boost data analysis and knowledge discovery, integrating diverse multimodal datasets (imaging, clinical, wearable, etc.) for a more holistic understanding of ophthalmic diseases. • Partner with Clinical Development and Medical Affairs to integrate RWE into evidence generation strategies. • Support trial optimization and regulatory submissions by incorporating insights from large-scale clinical datasets, electronic health records (EHRs), and other real-world data sources. • Build strong cross-functional collaborations within the company and spearhead external partnerships with academic institutions, technology providers, regulators, and industry consortia.

• We are seeking a visionary Director, R&D Neuroscience Data, Data Science & Artificial Intelligence – Ophthalmology to join the Neuroscience Data, Data Science & Artificial Intelligence (DDSAI) team. • This leader will shape and execute innovative strategies leveraging multimodal data sources, digital health technologies, computer vision, artificial intelligence (AI), and clinical/real-world evidence (RWE) to accelerate drug discovery and development, and maximize patient impact. • By combining ophthalmology expertise with strong data science acumen, this role will enhance clinical trial execution and ensure that new solutions are patient-centric and ready for regulatory and payer acceptance. • As an integral member of a highly matrixed team, the Director will collaborate with cross-functional experts in the Neuroscience Therapeutic Area, Clinical Development, Quantitative Sciences, Regulatory Affairs, and Patient-Reported Outcomes, and forge strategic external partnerships to infuse new ideas and capabilities. • This is a unique opportunity to redefine how we understand and treat eye diseases—uncovering novel digital biomarkers and endpoints, stratifying patients for more personalized care, and ultimately delivering better outcomes for people living with ophthalmic diseases.