Data Scientist

Senior Data Scientist

Hyatt is seeking a Senior Data Scientist to lead the development of advanced Machine Learning, Natural Language Processing (NLP), Artificial Intelligence, and Operations Research solutions supporting enterprise Risk Management, Claims Analytics, Incident Mitigation, and business optimization initiatives. This role will partner closely with Risk Management, Legal, Data Engineering, Data Governance, BI, and MLOps teams to design, deploy, and optimize predictive and optimization models that directly impact business outcomes. This is not a reporting-focused or dashboard-oriented Data Scientist role. Hyatt is specifically looking for a hands-on practitioner capable of building production-grade AI and Machine Learning solutions that can identify high-risk incidents, predict claim severity, optimize business decisions, and deliver explainable insights to business stakeholders.

Hyatt is not looking for a generic Data Scientist. They are hiring an AI & Risk Analytics Specialist who can build production-ready machine learning, NLP, and optimization models that help Hyatt predict, prioritize, and mitigate risk before incidents become costly claims. The primary mission of this role is to: Predict which incidents are most likely to become claims, Forecast claim severity and financial exposure, Analyze unstructured incident and claims narratives using NLP and LLM technologies, Develop explainable AI solutions that business stakeholders can trust, Apply Operations Research techniques to optimize business decisions and resource allocation, Deliver scalable production models that integrate into enterprise workflows.

Core Responsibilities:

Machine Learning & Predictive Modeling: Design, develop, deploy, and optimize machine learning models, Build incident prioritization and claim severity prediction models, Develop risk-scoring frameworks for proactive risk identification, Perform feature engineering across structured and unstructured datasets, Monitor model performance, drift, retraining requirements, and scoring quality.

Natural Language Processing (NLP) & AI: Develop NLP solutions for claims and incident narrative analysis, Build text classification and language-processing pipelines, Leverage Large Language Models (LLMs) to extract business insights, Generate explainable AI outputs and risk-driver analysis, Apply AI techniques to improve operational decision-making.

Data Science & Advanced Analytics: Develop predictive analytics and statistical modeling solutions, Build record-linkage and entity-resolution models where unique identifiers do not exist, Support large-scale data analysis across enterprise datasets, Work with structured, semi-structured, and unstructured data sources.

Cross-Functional Collaboration: Partner with Risk Management, Legal, Data Engineering, Data Governance, BI, and MLOps teams, Translate business requirements into technical solutions, Present findings and recommendations to technical and executive stakeholders, Mentor junior Data Scientists and contribute to team best practices.

Documentation & Governance: Create documentation covering methodology, assumptions, validation approaches, and limitations, Support model governance and explainability requirements, Ensure compliance with data governance, privacy, and security standards.

Machine Learning Frameworks: Scikit-Learn, XGBoost, TensorFlow, PyTorch, MXNet, LLM Frameworks.

Cloud Platforms: AWS, Azure, GCP.

DevOps & MLOps: CI/CD, MLOps Frameworks, Model Deployment & Monitoring.

Education: Required: Master's Degree in: Computer Science, Statistics, Industrial Engineering, Operations Research, or Related Technical Field. Preferred: PhD in a relevant discipline.

Ideal Candidate Profile: The strongest candidates will demonstrate: Deep Operations Research expertise, Strong AI/ML engineering capabilities, Hands-on NLP and LLM experience, Experience building production machine learning systems, Claims, risk, or incident analytics experience, Ability to communicate complex analytical findings to business stakeholders, Strong understanding of model explainability and governance, Experience deploying scalable enterprise AI solutions. This role is best suited for a senior-level Data Scientist who can move beyond experimentation and deliver measurable business value through production-ready AI, NLP, and optimization solutions.