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UL
UL LLC
Principal Engineer - Digital AI, Consumer TIC
Career Insights for Data Scientist
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Based on Illinois data
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What they do
A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.
$101,074 / year median in Illinois
+16% projected growth
Job Description
Principal Engineer - Digital AI, Consumer TIC serves as the Global Subject Matter Expert for Digital AI technologies, enabling UL Solutions to develop standards, certification, assurance, training, advisory, and assessment services across digital AI systems. This role provides technical leadership for AI governance, enterprise and cloud AI, predictive and Generative AI, foundation and frontier models, AI agents and agentic workflows, multi-agent systems, and AI lifecycle assurance. The position translates emerging technologies, regulations, standards, and customer needs into scalable UL Solutions offerings that strengthen the company's AI standards and services portfolio. Must Have Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering, Information Systems, or a related technical discipline. Advanced degree preferred. 15+ years of technical experience in software platforms, cloud systems, AI/ML systems, cybersecurity, technology risk, model lifecycle management, digital assurance, or related technology domains, including 5+ years of direct experience working with AI technologies. Demonstrated experience translating AI governance, model risk management, and regulatory requirements into practical evaluation methodologies, controls, assurance frameworks, certification programs, or customer-facing services. Demonstrated ability to evaluate and test AI systems and identify failure modes, risks, and control gaps, including model performance, transparency, traceability, explainability, reliability, robustness, human-in-the-loop controls, fairness and bias, privacy, security, accountability, and operational resilience. Working knowledge of AI evaluation and testing methodologies, including benchmarking, scenario-based testing, red teaming, bias and fairness evaluation, robustness testing, model monitoring, and assessment of risk-mitigation controls. Working knowledge of predictive AI and machine learning, Generative AI, Large Language Models (LLMs), foundation and frontier models, retrieval-augmented generation (RAG), AI agents, multi-agent systems, and associated technical, operational, and governance risks, including hallucinations, prompt injection, jailbreaks, data leakage, model drift, unsafe actions, and output reliability. Strong presentation, technical writing, thought leadership, and stakeholder engagement capabilities. Preferred to Have Advanced degree in Artificial Intelligence, Machine Learning, Robotics, Computer Science, Data Science, or a related technical discipline. Familiarity with AI governance frameworks, standards, and emerging global AI regulatory requirements. Experience developing or applying standards, conformity assessment methodologies, certification programs, audit frameworks, testing approaches, technical requirements, or commercial services for emerging technologies. Demonstrated ability to influence customers, regulators, standards committees, industry forums, cross-functional business leaders, and technical stakeholders.