• Manage and scale a high performing team of data scientists • Coach and mentor data scientists of varying experiences to ensure their continued growth • Plan, execute, and deliver a portfolio of mission-critical data science projects for Dash Experience, and provide technical leadership in a fast-paced environment • Leverage data-driven insights to proactively identify most impactful opportunities, and directly influence product roadmaps and strategies • Translate complex data insights into implications and recommendations for the business via excellent communication skills, both verbal and written • Work with cross-functional teams (including Product, Engineering, Design, User Research, and senior executives) to rapidly execute and iterate • Partner with Product Engineers and Data Engineers to build the reliable, efficient, and scalable data foundations, tools, and processes to drive our AI/ML capabilities’ long-term growth • Identify what matters most and prioritize ruthlessly

• 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.