Interested in this Data Scientist role at Carnival Cruise Line?
Apply Now →About This Role
The Data Scientist will support Casino Operations Analytics Team by applying machine learning and advanced analytics to improve operational decision\-making and guest experience. Key responsibilities include: Translating operational business questions into analytical and machine learning solutions. Owning projects end\-to\-end: data exploration, cleaning, feature engineering, modeling, validation, and deployment. Building predictive models for use cases such as: Optimizing product offerings and mix. Analyzing guest play behavior and segmentation. Predicting retention and lifetime value. Demand forecasting and operational planning. Supporting experimentation and incentive effectiveness analysis. Ensuring data integrity and model reliability. Communicating insights clearly to both technical and business stakeholders. Proactively identifying opportunities to improve analytical capabilities within the team. This role offers growth potential, including the opportunity to help expand and shape the team’s data science capabilities over time.
Essential Functions:
- Develop and Implement Predictive \& Analytical Models – Design, build, validate, and refine machine learning and statistical models to address operational business problems such as demand forecasting, guest behavior analysis, and product optimization.
- Data Exploration, Cleaning, and Validation – Perform in\-depth data exploration, quality assessment, cleaning, transformation, and feature engineering to ensure reliable inputs for modeling and analysis
- Translate Business Requirements into Analytical Solutions – Collaborate with operations and analytics stakeholders to define problem statements, success metrics, and analytical approaches aligned with business objectives.
- Deploy and Maintain Production\-Ready Models – Support deployment of models into operational environments, monitor performance, and implement improvements to ensure accuracy and reliability over time.
- Conduct Experimentation and Performance Analysis – Design and evaluate experiments (A/B testing) and assess effectiveness of operational initiatives, incentives, and product strategies.
- Demand Forecasting and Operational Planning Support – Develop forecasting models and scenario analyses to support staffing, capacity planning, and operational decision\-making.
- Communicate Insights and Recommendations – Prepare clear summaries, visualizations, and presentations to communicate findings to both technical and non\-technical stakeholders.
- Proactively Identify Opportunities for Analytical Improvement – Identify areas where advanced analytics or machine learning can enhance operational efficiency, guest engagement, or product performance.
Knowledge, Skills \& Abilities:
- Scope: Applies advanced analytics and machine learning to optimize casino operations, enhance guest experience, and support data‑driven decision‑making across forecasting, segmentation, experimentation, and product strategy.
- Problem solving: Translates complex operational questions into analytical frameworks, builds and validates predictive models, and resolves data quality and model‑performance issues across multiple systems and stakeholders.
- Impact: Drives measurable improvements in operational efficiency, guest engagement, and revenue outcomes by delivering accurate forecasts, actionable insights, and reliable production‑ready models.
- Leadership: Leads end‑to‑end analytical initiatives, elevates team capabilities through proactive innovation, and communicates technical findings clearly to influence decisions across both technical and business partners.
Qualifications:
- Bachelor's Degree Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, or related quantitative field required. Masters Degree Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, or related quantitative field preferred.
- Minimum 4 years of hands\-on experience in data science or applied machine learning. Experience building predictive models using real\-world, large\-scale structured data. Experience delivering models into production or operational environments. Experience working cross\-functionally with business stakeholders. Prior casino or gaming industry experience is not required.
Travel: Less than 25% with shipboard travel likely
Work Conditions: Work primarily in a climate\-controlled environment with minimal safety/health hazard potential.
Physical Demands Must be able to remain in a stationary position at a desk and/or computer for extended periods of time.
This position is classified as “in\-office.” As an in\-office role, it requires employees to work from a designated Carnival office in South Florida Monday through Thursday each week. Employees may work from their homes on Fridays. Candidates must be located in (or willing to relocate to) the Miami/Ft. Lauderdale area.
Offers to selected candidates will be made on a fair and equitable basis, taking into account specific job\-related skills and experience.
At Carnival, your total rewards package is much more than your base salary. All non\-sales roles participate in an annual cash bonus program, while sales roles have an incentive plan. Director and above roles may also be eligible to participate in Carnival’s discretionary equity incentive plan. Plus, Carnival provides comprehensive and innovative benefits to meet your needs, including:
- Health Benefits:
+ Cost\-effective medical, dental and vision plans
+ Employee Assistance Program and other mental health resources
+ Additional programs include company paid term life insurance and disability coverage
- Financial Benefits:
+ 401(k) plan that includes a company match
+ Employee Stock Purchase plan
- Paid Time Off
+ Holidays – All full\-time and part\-time with benefits employees receive days off for 8 company\-wide holidays, plus 2 additional floating holidays to be taken at the employee’s discretion.
+ Vacation Time – All full\-time employees at the manager and below level start with 14 days/year; director and above level start with 19 days/year. Part\-time with benefits employees receive time off based on the number of hours they work, with a minimum of 84 hours/year. All employees gain additional vacation time with further tenure.
+ Sick Time – All full\-time employees receive 80 hours of sick time each year. Part\-time with benefits employees receive time off based on the number of hours they work, with a minimum of 60 hours each year.
- Other Benefits
+ Complementary stand\-by cruises, employee discounts on confirmed cruises, plus special rates for family and friends
+ Personal and professional learning and development resources including tuition reimbursement
+ On\-site Fitness center at our Miami campus
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About Us
Carnival Corporation \& plc is the world’s largest leisure travel company, our mission to deliver unforgettable happiness to our guest through our diverse portfolio of leading cruise brands and island destinations, including Carnival Cruise Line, Holland America Line, Princess Cruises, and Seabourn in North America and Australia; P\&O Cruises and Cunard Line in the United Kingdom; AIDA in Germany; Costa Cruises in Southern Europe.
Join us and embark on a career that offers not only the chance to grow professionally but also the opportunity to be part of a global community that makes a difference.
In addition to other duties/functions, this position requires full commitment and support for promoting ethical and compliant culture. More specifically, this position requires integrity, honesty, and respectful treatment of others, as well as a willingness to speak up when they see misconduct or have concerns.
Carnival Corporation \& plc and Carnival Cruise Line is an equal employment opportunity/affirmative action employer. In this regard, it does not discriminate against any qualified individual on the basis of sex, race, color, national origin, religion, sexual orientation, age, marital status, mental, physical or sensory disability, or any other classification protected by applicable local, state, federal, and/or international law.
https://www.dol.gov/sites/dolgov/files/WHD/legacy/files/eppac.pdf
https://www.dol.gov/sites/dolgov/files/WHD/legacy/files/fmlaen.pdf
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 2,799 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Carnival Cruise Line, 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 in Demand for This Role
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 $200,350 based on 604 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $159,385.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Carnival Cruise Line AI Hiring
Carnival Cruise Line has 1 open AI role right now. They're hiring across Data Scientist. Based in Miami, FL, US.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 median).
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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $200,000. Top-quartile roles start at $252,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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
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