Senior Data Scientist

Insight Global is looking for a Senior Data Scientist for one of our logistics clients. As the senior data scientist, you will design, build, and deploy Generative AI solutions, including AI assistants, RAG-enabled applications, and agentic AI workflows. You will integrate LLMs with enterprise data and systems, optimize AI performance through prompt engineering and model evaluation, and partner with engineering, product, cybersecurity, and business teams to move solutions from proof of concept into production.

• Bachelor’s degree or a comparable combination of relevant professional experience.
• 5+ years of experience working in Data Science, Applied AI, Machine Learning, or a related area, with exposure to Generative AI, LLM-based applications, or AI agents.
• Hands-on experience with at least one technical language or platform such as Python, SQL, R, Java, Spark, or SAS, ideally within an AI, machine learning, or data-focused environment.
• Working knowledge of core machine learning concepts and techniques, including supervised and unsupervised learning approaches.
• Ability to understand technical AI capabilities and translate them into practical applications that address business needs.
• Strong written and verbal communication skills, with the ability to explain technical concepts clearly to both technical and non-technical stakeholders.
• Degree in Computer Science, Engineering, Mathematics, Statistics, Data Science, Economics, or another quantitative or technical discipline.
• Advanced degree, such as a Master’s or PhD, in AI, Computer Science, Data Science, Machine Learning, or a related field.
• 3+ years of professional experience across Data Science, Machine Learning, Applied AI, Generative AI, or similar technical disciplines.
• Experience developing or supporting production-ready GenAI solutions such as AI assistants, conversational applications, copilots, or autonomous/agent-based solutions.
• Strong Python experience, including the use of common AI, ML, or data science libraries and frameworks.
• Exposure to Retrieval-Augmented Generation (RAG), including using LLMs with documents, databases, knowledge bases, or other organizational data.
• Understanding of agentic AI patterns, including API/tool integration, orchestration, and AI-driven multi-step workflows.
• Experience with the broader AI/ML lifecycle, including development, testing, evaluation, deployment, and ongoing performance improvement