• Develop scalable pipelines for text ingestion, cleaning, normalization, and tokenization to support downstream applications. • Architect and maintain robust indexing systems and vector databases for semantic search and retrieval. • Create reusable prompting strategies and lead fine-tuning initiatives for LLMs tailored to business-specific tasks. • Construct dynamic knowledge systems and agentic workflows using LangChain and LangGraph. • Apply VRAG and GraphRAG design patterns to enrich information retrieval and contextual understanding. • Perform benchmark testing and model evaluations to improve accuracy, efficiency, and scalability of NLP systems. • Work closely with engineering, product, and research stakeholders to deliver integrated AI-driven features. • Mentor junior data scientists, guide best practices, and drive innovation across AI projects.

• Lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization. • Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives. • Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights. • Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy. • Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization. • Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise. • Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.

• Contribute to the development and continuous improvement of data infrastructure for clinical studies and real-world data sources. • Lead the research, prototyping, and development of machine learning and AI models aligned to business needs. • Oversee the production and review of tables, figures, and listings, interpret results, and proactively address limitations in study design or statistical methods. • Provide statistical consultation and thought leadership to cross-functional partners in R&D, engineering, marketing, manufacturing, medical science, and with external collaborators.

• Suggest and analyze Lifecycle Marketing campaigns rooted in clients’ delight and friction. • Employ and exemplify rigor in mathematical analyses and sound design principles in software development. • Collaborate with the Lifecycle Marketing team to define quarterly and yearly initiatives. • Turn initiatives into projects with clear rationale, candidate approaches and milestones. • Execute planned projects through hands-on work and delegation. This includes tracking priority and progress of projects in weekly meetings. • Articulate technical information, including assumptions, methodologies, and outcomes to specialists across disciplines. Translate hunches to hypotheses and numbers into recommendations. • Absorb and articulate the Marketing strategy and project rationales to Data Scientists.