Drive advanced analytics, causal reasoning, and AI-powered decision intelligence across multiple use cases within the AI portfolio
Work at the intersection of data science, machine learning, and GenAI, turning complex data into actionable insights
Design and deploy systems that integrate experimentation, observational data, machine learning, and generative AI into decision-making pipelines
Collaborate closely with data engineers, ML engineers, analysts, and platform teams, contributing to shared modeling standards and cross-functional AI architecture
Requirements
Strong experience in machine learning, statistics, and applied data science
Experience with causal inference, experimentation, or decision science methodologies
Solid understanding of forecasting, optimization, or analytical modeling techniques
Strong programming skills in Python and SQL
Experience building and deploying production-ready data science or ML systems
Familiarity with model lifecycle management (training, deployment, monitoring)
Hands-on experience with at least one major cloud platform: Azure (preferred), AWS, or GCP
Experience working with modern data and AI platforms (e.g., Azure ML / Azure AI, Databricks, or similar ecosystems)
Experience working with complex, multi-source datasets (e.g., transactional, behavioral, operational data)
Ability to translate business problems into analytical frameworks
Strong problem-solving skills with focus on business impact
Ability to translate complex models into actionable decisions
Strong collaboration and communication skills across technical and business teams