Data Scientist – AI

Job Overview

Serve as a dedicated Data Scientist within the specialized Miral Destination AI team, working exclusively on initiatives that advance Miral Destination. Analyze Miral Destination data, develop and fine-tune AI and machine learning models, and generate actionable insights that directly support Miral Destination’s business objectives. Partner closely with the ML Ops Engineer to move models from experimentation into production, and report to the Senior Manager AI to ensure all work aligns with the destination’s AI roadmap and priorities.

Job Scope

• Analyze Miral Destination datasets to uncover patterns, trends, and opportunities that inform destination decisions

• Develop, train, and fine-tune AI and machine learning models tailored to Miral Destination use cases

• Build RAG pipelines, vector databases, and LLM-based solutions on Databricks for Miral Destination applications

• Generate clear, actionable insights and visualizations that directly support Miral Destination business objectives

• Collaborate with the ML Ops Engineer to operationalize models, ensuring they are production-ready and monitored

• Translate requirements from Miral Destination stakeholders, via the Senior Manager AI, into data science deliverables

• Align all work with the enterprise AI architecture, governance, security, and data management standards set by the AI & Data organization

• Uphold data quality, model performance, governance, and responsible-AI standards across all Miral Destination work

• Collaborate with AI, Data Engineering, BI, and Enterprise Data teams across DTD to leverage shared capabilities, reusable assets, common platforms, and best practices.

Job Essentials

• 3–5 years in Data Science, Machine Learning, or Advanced Analytics

• Demonstrated experience taking models from analysis through to deployment

• Strong data analysis, statistical modeling, and machine learning skills

• Hands-on experience with Python and Databricks (incl. MLflow)

• AI model development, fine-tuning, and evaluation

• Understanding of RAG, LLMs, and vector databases

• Data storytelling – turning analysis into clear, actionable insights

• Collaboration with ML Ops to deploy and monitor models

Education

• Bachelor’s or Master’s in Data Science, AI, Statistics, Computer Science, or related field