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The Opportunity
Hyatt Hotels Corporation seeks an enthusiastic Lead Data Scientist to join our AIML Team. In this role, you will be collaborating closely with our partners across ML Engineering, Data Engineering, Platform, Product, and Finance teams. You'll be instrumental in continuing to make Hyatt a leading AIML powered hospitality company and be a part of the team that is passionate about our purpose, committed to nurturing curiosity and new skills, and building connections across the organization with colleagues, customers, and guests.
Who We Are
At Hyatt, we believe in the power of belonging and creating a culture of care, where our colleagues become family. Since 1957, our colleagues and our guests have been at the heart of our business and helped Hyatt become one of the best and fastest-growing hospitality brands in the world. Our transformative growth and the addition of new hotels, brands, and business lines can open the door for exciting career and growth opportunities for our colleagues.
As we continue to grow, we never lose sight of what's most important: People. We turn trips into journeys, encounters into experiences, and jobs into careers.
Why Now?
This is an exciting time to be at Hyatt. We are growing rapidly and are looking for passionate changemakers to be a part of our journey. The hospitality industry is resilient and continues to offer dynamic opportunities for upward mobility, and Hyatt is no exception.
How We Care for Our People
What sets us apart is our purpose-to care for people so they can be their best. Every business decision is made through the lens of our purpose, and it informs how we have and will continue to support each other as members of the Hyatt family. Our care for our colleagues is the key to our success. We're proud to have earned a place on Fortune's prestigious 100 Best Companies to Work For® list since 2013. This recognition is a testament to the tremendous way our Hyatt family continues to come together to care for one another, our commitment to a culture of inclusivity, empathy, and respect, and making sure everyone feels like they belong.
We're proud to offer exceptional corporate benefits which include:
• Annual allotment of free hotel stays at Hyatt hotels globally
• Flexible work schedule
• Work-life benefits including wellbeing initiatives such as a complimentary Headspace subscription, and a discount at the on-site fitness center
• A global family assistance policy with paid time off following the birth or adoption of a child as well as financial assistance for adoption
• Paid Time Off, Medical, Dental, Vision, 401K with company match
Who You Are
As our ideal candidate, you understand the power and purpose of our culture of care, and embody our core values of Empathy, Inclusion, Integrity, Experimentation, Respect, and Wellbeing. You enjoy working with others, are results-driven, and are looking for a variety of opportunities to develop personally and professionally.
The Role
As a Lead Data Scientist working on Search, Personalization and Agents, you will own the design, development, evaluation, and optimization of AI and Machine Learning solutions that support Hyatt's guest, colleague, and operational experiences.
This is an individual contributor role with no direct people-management responsibilities. However, you will be expected to provide technical leadership, mentor peers, influence architecture and product direction, and raise the overall technical bar for applied AI at Hyatt.
Generative AI and Applied Machine Learning
• 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.
• Identify opportunities to apply AI to improve guest experiences, colleague productivity, operational efficiency, and commercial outcomes.
• Translate ambiguous business problems into clear data science problem statements, solution designs, success metrics, and implementation plans.
Technical Leadership as an Individual Contributor
• Serve as a hands-on technical lead for high-impact AI and machine learning initiatives.
• Lead solution design, modeling decisions, experimentation strategy, and technical tradeoff discussions.
• Partner with ML engineering and data engineering teams to deploy scalable real-time inference pipelines and batch processing workflows.
• Influence technical roadmaps and help sequence data science initiatives based on business value, feasibility, risk, and team capacity.
• Mentor data scientists and ML practitioners through design reviews, code reviews, modeling best practices, and knowledge sharing.
Production AI, MLOps, and Cloud Delivery
• Collaborate with ML engineering to productionize models and Gen AI services using AWS-native tools and modern MLOps practices.
• Contribute to scalable ML system design, including data pipelines, feature workflows, model serving, observability, monitoring, and lifecycle management.
• Apply strong software engineering practices, including version control, CI/CD, testing, reproducibility, containerization, and documentation.
• Support deployment patterns for both batch and low-latency inference use cases.
• Partner with security, governance, architecture, and legal/privacy stakeholders to ensure AI systems are reliable, secure, compliant, and responsibly deployed.
Cross-Functional Collaboration
• Work closely with product owners, data scientists, ML engineers, data engineers, architects, and business stakeholders to deliver end-to-end algorithmic products.
• Communicate model behavior, limitations, assumptions, risks, and business impact clearly to technical and non-technical audiences.
• Define measurable success criteria and help evaluate whether AI solutions are delivering intended outcomes.
• Champion responsible AI, inclusive design, and practical experimentation across projects.