Lead Data Scientist

Responsibilities

• Own the design, development, evaluation, and optimization of AI and ML solutions that support Hyatt’s guest, colleague, and operational experiences.
• Provide technical leadership, mentor peers, influence architecture and product direction, and raise the overall technical bar for applied AI at Hyatt.
• 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.
• 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.
• 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.

Requirements

• Master’s degree in computer science, software engineering, or a related field; PhD preferred.
• Six or more years of experience in machine learning roles with a focus on NLP/NLU, reinforcement learning, or large language model applications.
• Three or more years of people management experience in a tech leadership role.
• Experience fine-tuning and deploying LLMs or other Generative AI solutions to production.
• Proficiency with AWS cloud services such as SageMaker, ECS/EKS, Step Functions, Lambda, and Glue.
• Strong Python programming skills, with familiarity in SQL, PySpark, and containerization using Docker.
• Proven experience designing scalable data pipelines and ML systems for both real-time and batch inference.
• Deep understanding of responsible AI practices, CI/CD pipelines, Agile development practices, and model lifecycle management.
• Excellent interpersonal and communication skills, with a strong bias for action and collaboration.
• Familiarity with ML observability and governance tools.

Technologies

• Python
• SQL
• PySpark
• Docker
• SageMaker
• ECS
• EKS
• Step Functions
• Lambda
• Glue

Benefits

• Annual allotment of free hotel stays at Hyatt hotels globally
• Flexible work schedule
• Headspace subscription
• Discount at on-site fitness center
• Paid time off following the birth or adoption of a child
• Financial assistance for adoption
• Paid Time Off
• Medical, Dental, Vision coverage
• 401K with company match

Qualifications

• Master’s degree in computer science, software engineering, or a related field; PhD preferred.
• Six or more years of experience in machine learning roles focused on NLP/NLU, reinforcement learning, or LLM applications, including at least three years in a tech leadership role.
• Experience fine-tuning and deploying LLMs or other Generative AI solutions to production.
• Expertise in AWS cloud services (SageMaker, ECS/EKS, Step Functions, Lambda, Glue).
• Strong Python skills with SQL, PySpark, and Docker experience.
• Proven ability to design scalable data pipelines and ML systems for real-time and batch inference.
• Deep understanding of responsible AI, CI/CD, Agile methods, and model lifecycle management.
• Excellent communication and collaboration skills.
• Familiarity with ML observability and governance tools.