Interested in this Data Scientist role at Hyatt?
Apply Now →Skills & Technologies
About This Role
Summary
-----------
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.
Qualifications
------------------
Experience Required:
- Master’s degree in computer science, Software Engineering, or related field. Ph.D preferred.
- 6\+ years of experience in machine learning roles focused on areas such as NLP/NLU, reinforcement learning or LLM applications, including 3\+ years of people management experience in a tech leadership role.
- Experience in fine\-tuning and deploying LLMs or other Generative AI solutions to production.
- Expertise in AWS cloud services (e.g., SageMaker, ECS/EKS, Step Functions, Lambda, Glue).
- Strong programming skills in Python, with experience in SQL, PySpark, and containerization (e.g., 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.
The position responsibilities outlined above are in no way to be construed as all\-encompassing. Other duties, responsibilities, and qualifications may be required and/or assigned as necessary.
We welcome you:
Research shows that individuals tend to apply to jobs only if they meet all the listed job qualifications. Unsure if you check every box, but feeling inspired to enhance your career? Apply. We’d love to consider your unique experiences and how you could make Hyatt even better.
*We value our relationships with recruitment partners and require that agencies contact us first before submitting any candidates. Hyatt will not be responsible for any fees and obligations associated with unsolicited submissions unless a formal agreement is in place.*
The salary range for this position is $160,000 \-170,000\. This position is also eligible to earn an annual bonus.
*The final pay rate/salary offered to the successful candidate will depend on experience, skill level and other qualifications for the role, as well as the location of the performance of work. Pay for the successful candidate will meet local requirements, including the local minimum wage rate.*
Salary Context
This $160K-$170K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Hyatt, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($165K) sits 14% below the category median. Disclosed range: $160K to $170K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Hyatt AI Hiring
Hyatt has 1 open AI role right now. They're hiring across Data Scientist. Based in Chicago, IL, US. Compensation range: $170K - $170K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.