Senior Lead Data Scientist

Journey with us! Combine your career goals and sense of adventure by joining our exciting team of employees. Royal Caribbean Group is pleased to offer a competitive compensation and benefits package, and excellent career development opportunities, each offering unique ways to explore the world.

 

Position Summary:

We are seeking a Senior Lead Data Scientist to lead cross-functional AI/ML initiatives and bridge tactical execution and medium‑term strategy. The position demands an inquisitive individual who can work both independently and alongside other data analytics professionals, both internal and external to Royal Caribbean, to support the development of AI/ML business assets and provide regular analytics updates with senior stakeholders. This position requires high technical competency in AI modeling, including PySpark + ML models, as well as strong written & oral communication skills. The ideal candidate combines expert-level modeling and software engineering skills with strong stakeholder management and program leadership.

 

Essential Responsibilities:

• Lead end-to-end delivery for a portfolio of AI/ML and generative AI projects — from discovery, problem framing and data strategy through feature engineering, modeling, deployment, monitoring, and business adoption; be hands‑on when required to deliver production‑ready solutions.
• Develop, own and adapt a 1–2 year roadmap; prioritize projects by expected business impact, feasibility, cost and timing; author business cases and secure stakeholder buy‑in.
• Own measurable business outcomes for assigned programs (critical KPIs) and drive ROI through experimentation and measurement strategies (A/B tests, uplift analysis, etc.).
• Oversee, organize, coach and mentor a team of data scientists, ML engineers and technical leads; enforce engineering best practices (code reviews, testing, documentation, reproducibility).
• Provide technical leadership and project management; act as primary strategy contact and escalation point for stakeholders up to executive level; manage change, urgent requests and deliver within agile programs.
• Perform hands‑on development of complex analytic systems, predictive and optimization models, custom algorithms and large‑scale data analyses to uncover insights and drive business value.
• Design, implement and continuously improve robust MLOps and production pipelines: CI/CD, model/feature/versioning/lineage, deployment/rollback, monitoring/alerting, retraining pipelines and SLA management.
• Ensure ongoing technical and cost performance of production models — drift detection, monitoring, retraining triggers, compute/cost optimization and SLA enforcement.
• Lead architecture, tooling and platform decisions for the data science stack; evaluate build vs. buy tradeoffs, assess vendors/partners and recommend open‑source or commercial solutions.
• Partner cross‑functionally (Product, Engineering, Data Engineering, Operations, Commercial) to operationalize models, integrate with core systems and measure/validate business impact.
• Drive ideation and discovery for new analytics and AI products/features; prototype PoCs, iterate with stakeholders and shepherd prioritized initiatives into production.
• Translate technical results into clear business recommendations and ROI; present tradeoffs, risks and outcomes to both technical and non‑technical audiences, including senior executives.
• Leverage advanced ML techniques (statistical modeling, deep learning, NLP, computer vision, recommendation systems, optimization, large‑scale data mining) to solve business problems.
• Lead generative AI initiatives: design, fine‑tune and deploy LLMs and other generative models; build prompt engineering practices, retrieval‑augmented generation (RAG), embedding pipelines and vector DB integrations.
• Establish governance, safety and risk controls for generative and other AI: content moderation, hallucination mitigation, bias/fairness testing, privacy/PII safeguards, legal/compliance review and human‑in‑the‑loop processes.
• Define evaluation frameworks and KPIs for generative and predictive models (quality, relevance, safety, bias, latency, cost); implement continuous testing, validation and experiment‑driven improvements.
• Incorporate generative AI-specific practices into MLOps: prompt/version control, fine‑tuning pipelines, inference scaling (latency/throughput), cost/performance optimization, m