Data Scientist (GenAI Solutions) - Hybrid

XPO•Boston, MA

1d•Hybrid

About The Position

This role focuses on building and deploying Generative AI (GenAI) solutions within XPO, a global transportation services provider. The Data Scientist will be responsible for developing GenAI chatbots, AI assistants, and RAG-enabled applications to enhance employee access to information and automate business tasks. The position involves integrating LLMs with enterprise data sources, building Agentic AI workflows, and optimizing conversational AI performance. Collaboration with various teams, including Engineering, Product, and Cybersecurity, is crucial for moving solutions from concept to production. The role also involves researching emerging AI technologies to identify new opportunities for improving productivity and business performance.

Requirements

Bachelor’s degree or equivalent related work or military experience
1 year of experience in data science or applied AI, with a focus on Generative AI, Agentic AI, or LLM-powered applications
Strong communication skills with the ability to translate complex AI capabilities into clear business use cases and recommendations
Experience in at least one programming language such as Python, R, SQL, SAS, Spark, Java, etc., with practical applications in AI or ML projects
Familiarity with AI techniques and machine learning algorithms, including supervised and unsupervised learning
Strong communication skills with a knack for translating complex AI concepts into clear business insights

Nice To Haves

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or any related quantitative discipline
A Master’s or PhD in Computer Science, AI, Data Science, or a related quantitative field
3+ year of experience in Data Science, Applied AI, Machine Learning, Generative AI, or a related technical field
Experience building and deploying enterprise-scale LLM-powered chatbots, copilots, virtual assistants, or AI agents
Experience using Python and relevant AI/ML libraries and frameworks to develop data science or AI solutions
Familiarity with Retrieval-Augmented Generation (RAG) and connecting LLMs to enterprise documents, databases, knowledge repositories, or other internal data sources
Familiarity with Agentic AI concepts, including tool calling, API integration, workflow orchestration, and multi-step AI workflows
Understanding of machine learning and AI concepts, including model development, testing, evaluation, and deployment

Responsibilities

Build, deploy, and scale GenAI chatbots and AI assistants across the organization to improve how employees access information and complete business tasks
Develop RAG-enabled applications that securely connect LLMs with enterprise documents, databases, and internal knowledge sources
Build Agentic AI workflows that use tools, APIs, and enterprise systems to automate multi-step processes
Improve conversational AI performance through prompt engineering, retrieval strategies, model evaluation, and response optimization
Help implement AI guardrails, monitoring, and governance to ensure solutions are accurate, secure, scalable, and reliable
Integrate GenAI solutions with enterprise applications, data platforms, and internal systems
Partner with Engineering, Product, Cybersecurity, and business teams to move solutions from proof of concept through production deployment
Research emerging GenAI, LLM, RAG, and Agentic AI technologies and identify new opportunities to improve productivity, operational efficiency, and business performance