Associate Data Scientist

We are seeking a Data Science Analyst to support end-to-end data science delivery by translating business questions into analyses, model components, and production-ready outputs under guidance across our cruise line client. This role emphasizes Data Science ownership of delivered business value: framing the right problem, building and validating ML/optimization/GenAI solutions, partnering on deployment, monitoring performance, and driving adoption in production. The ideal candidate combines statistical and machine learning depth with practical business judgment, strong stakeholder partnership, and the ability to convert analytical work into measurable outcomes rather than isolated prototypes.

• Problem Framing & Value: Frame high-impact business problems for entry-level analytics and supervised model delivery into measurable data science opportunities with clear decision owners, baseline metrics, adoption paths, and expected value tied to decision support, operational visibility, and early-stage automation.
• Predictive Modeling: Develop forecasting, propensity, classification, and ranking models using Python, scikit-learn, XGBoost, LightGBM, Cat Boost, and Databricks feature workflows to support production decisions.
• Prescriptive Decisioning: Build recommendation, simulation, and optimization solutions using MILP, heuristics, dynamic programming, or scenario modeling to improve operational and commercial decisions.
• GenAI Solutions: Design GenAI workflows using GPT-class models, Azure AI Foundry, RAG, embeddings, prompt engineering, and evaluation routines where natural-language or agentic capabilities improve business productivity.
• Statistical Experimentation: Design and evaluate A/B tests, quasi-experiments, causal analyses, bootstrap methods, and non-parametric tests to determine whether model or process changes create measurable lift.
• Explainability & Trust: Apply SHAP, sensitivity analysis, model diagnostics, error analysis, and stakeholder-ready explanations so users understand model behavior, limits, and decision implications.
• Production Deployment: Partner with AI Engineering to deploy models and analytical applications through Databricks, Azure ML, MLflow, APIs, or containerized services while retaining accountability for business value and model behavior.
• Production Operations: Monitor accuracy, drift, bias, adoption, latency, cost, and business KPIs; trigger retraining, recalibration, or process changes when performance or value realization degrades.
• Stakeholder Partnership: Partner with business, product, operations, AI Engineering, and data engineering teams to convert model outputs into decisions, workflows, incentives, and measurable adoption.

• Education: Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Operations Research, Engineering, Economics, or a related quantitative field, or equivalent practical experience.
• Experience: Demonstrated experience appropriate to entry scope delivering ML, optimization, experimentation, or GenAI solutions that moved beyond analysis into production use or business decisioning.
• ML Tooling: Hands-on experience with Python, scikit-learn, XGBoost, LightGBM, Cat Boost, PyTorch or Tensor Flow where appropriate, and model evaluation workflows for production-grade use cases.
• Optimization: Experience with MILP solvers, simulation, scenario planning, dynamic programming, heuristics, or prescriptive analytics methods applied to real business decisions.
• GenAI Platforms: Experience with Azure AI Foundry, GPT-class models, RAG, embeddings, prompt engineering, evaluation, and safe use of GenAI for decision support or workflow automation.
• Data Platforms: Advanced use of…