The Data and Analytics Team is seeking a highly skilled Lead Data Scientist with deep expertise in modern AI technologies. The ideal candidate will apply advanced knowledge in machine learning, natural language processing, and artificial intelligence to design, develop, and deploy models that accelerate innovation and support strategic decision-making. As part of the Enterprise Data and Analytics organization, the AI CoE drives measurable impact across the business through outcome-focused project execution. This newly created role will be dedicated to AI Automation value streams and lead data science initiatives from concept through deployment.

Key Responsibilities

• Collaborate with the Product Owner to manage the end-to-end AI development lifecycle—requirements gathering, data sourcing, model development, proof-of-concept demonstrations, risk governance reviews, and production deployment across both on-premises and AWS environments.
• Demonstrate strong self-discipline and professional ownership in managing project activities, maintaining transparent communication with leadership, seeking managerial guidance when appropriate, and proactively surfacing risks, blockers, or resource needs to ensure timely delivery.
• Apply automation techniques to streamline manual processes, enhance productivity and accuracy, and develop reusable, scalable self-service tools and assets.
• Stay current with emerging machine learning, cloud, and AI technologies, driving innovation by evaluating and integrating new capabilities into existing workflows and products.

Basic Requirements

• Master’s degree in Statistics, Economics, Data Science
• Five or more years related work experience in AWS cloud machine learning or a similar AI platforms, with familiarity in AWS Sagemaker, Lambda, S3, Athena

Preferred Skills and Characteristics

• PHD
• Proficiency in one or more programming languages, including Python (preferred) or R, Spark, and other modern data science tools.
• Working knowledge of Large Language Models (Llama, Claude, Titan), including experience with frameworks such as LangChain, vector databases, embeddings, and prompt engineering.
• Willingness to step outside traditional modeling tasks to solve broader technical or business challenges in support of team and organizational goals.
• Hands‑on experience with LLM fine‑tuning
• Practical understanding of advanced Retrieval‑Augmented Generation (RAG) methods such as MMR, multi‑vector retrieval, RAG‑fusion, HYDE, self‑RAG, and retrieval evaluation frameworks.
• Knowledge and practical experience with Agentic AI and agentic workflows, including task orchestration, multi‑agent collaboration, tool‑use agents, and workflow automation patterns.
• Hands‑on experience with advanced AWS AI/ML services such as Bedrock, JumpStart, Textract, and related ML orchestration capabilities

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