Über
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.
Sprachkenntnisse
- English
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