• Own the design, development, evaluation, and optimization of AI and Machine Learning solutions that support Hyatt’s guest, colleague, and operational experiences. • Design, prototype, and productionize Generative AI solutions in NL Search, Information Retrieval and Recommender Systems. • Build and evaluate LLM-powered applications, including retrieval-augmented generation, prompt engineering, fine-tuning, embeddings, semantic search, and agentic or workflow-based AI systems. • Develop robust model evaluation frameworks, including offline metrics, human evaluation, guardrail testing, bias and safety checks, and business-impact measurement. • Partner with ML engineering and data engineering teams to deploy scalable real-time inference pipelines and batch processing workflows. • Mentor data scientists and ML practitioners through design reviews, code reviews, modeling best practices, and knowledge sharing. • Collaborate with ML engineering to productionize models and Gen AI services using AWS-native tools and modern MLOps practices.

• Participate in the development and evolution of data products focused on Supply Chain, leading product direction and communications with your direct leadership; • Lead alignment sessions, problem exploration, and ideation with squads, stakeholders, partners, and users; • Deliver executive presentations that translate the team’s deliverables into business impact; • Prioritization and data analysis: prioritize initiatives and features based on data, business impact, and user feedback, analyzing quantitative and qualitative information to support decisions and product evolution; • Facilitate workshops, exercises, and meetings; • Build the product narrative to continuously influence and coordinate politically and relationally across the various interacting areas; • Ensure the quality of deliveries and maintain continuous dialogue with stakeholders regarding improvements, evolutions, features, and new capabilities of the data products; • Lead discussions and contribute ideas and suggestions in technical conversations with Analytics Engineering and Data Science teams; • Support leadership in the operational management of the team, especially by monitoring team progress and evolution metrics; • Organize the backlog and encourage technical team autonomy in applying Agile best practices; • Product lifecycle management: manage the product through all of its phases, from conception and discovery to development, launch, growth, maturity, and decommissioning.

• Own the end-to-end product marketing strategy for Live Query and Alteryx’s core data connectivity capabilities • Develop persona-specific narratives for IT architects, data engineers, and CDAO organizations • Partner with Product Management to shape the data connectivity roadmap narrative • Build launch plans, enablement materials, and customer content for new connectivity capabilities • Monitor the competitive landscape across data connectivity, ELT/ETL, and cloud data integration • Define and maintain the positioning and messaging framework for Alteryx’s OCR Tools and Intel Suite • Develop use-case-driven content—including demos, solution briefs, and customer stories • Partner with Solutions PMM to develop vertical-specific messaging and content • Own the go-to-market strategy for Alteryx’s Data Products and Connect capabilities • Develop messaging that resonates with both data producers and data consumers • Identify customer champions and develop case studies that showcase how Data Products and Connect have transformed enterprise data accessibility and analytical productivity • Maintain a deep, current understanding of the cloud data platform and data integration competitive landscape.

• Develop, validate, deploy, and monitor machine learning and generative AI models to deliver actionable, scalable insights and solutions • Design and implement generative AI solutions using LLMs, prompt engineering, RAG pipelines, and fine-tuning strategies. • Analyze complex datasets by performing data preparation, transformations, exploratory analysis, and feature engineering to uncover trends, patterns, and actionable insights that support advanced analytics and model development • Design and implement end-to-end data and ML pipelines (including model training, evaluation, deployment and monitoring within production environments), while partnering with data engineering to continuously enhance and maintain MLOps tools and processes. • Monitor and evaluate model performance using both quantitative metrics and human feedback loops. • Collaborate with business stakeholders to translate business needs into AI-driven solutions • Develop and deliver clear visualizations, dashboards, and reports that effectively communicate insights and drive business understanding and optimization. • Ensure data quality, model and data governance standards, and responsible AI practices are met, including bias mitigation, security and privacy. • Contribute to AI education, knowledge sharing, and innovation, staying current with emerging trends and translating them into practical applications.