• As a Data Scientist, your mission will go far beyond the numbers: you will be challenged to translate complex business needs into predictive models and intelligent solutions. • In a strategic capacity, you will take a leading role in evolving our ecosystem of financial and digital products, creating direct, large-scale impact on our customers' lives.

• Own the technical architecture and engineering strategy for AutoQuote's cloud-native platform — spanning AI feature integration, data pipeline infrastructure, microservices design, and platform reliability. • Lead the design and delivery of the highest-complexity, highest-impact engineering initiatives on the team, setting the architectural patterns and engineering standards that guide the broader organization. • Define and enforce software quality standards, AI usage guidelines, and engineering best practices across the AutoQuote engineering organization. • Partner with engineering leadership, product management, and program stakeholders to translate program strategy into a coherent technical roadmap and prioritized backlog. • Evaluate, prototype, and champion emerging AI capabilities — including LLM integration, agentic frameworks, and AI-assisted development tooling — driving adoption across the team and into the product. • Drive responsible AI governance across AutoQuote, including prompt engineering standards, AI output evaluation practices, and compliance with Cisco security and data handling policies. • Mentor and technically develop senior and mid-level engineers; serve as the primary technical authority and critical issue point for complex engineering decisions. • Lead architecture reviews, critical design decisions, and cross-functional technical alignment sessions. • Represent AutoQuote engineering in program-level and executive forums; communicate technical tradeoffs, risks, and decisions with clarity to both technical and non-technical audiences.

• 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.