Associate Data Scientist

UL Solutions•Northbrook, IL

6d•Remote

About The Position

The Associate Data Scientist supports the Operations Data Science & AI team at UL Solutions in developing predictive models, data-driven and AI solutions for laboratory, field engineering, and accreditation & compliance operations. The role covers the full innovation lifecycle - from ideation and prototyping to implementation and validation - combining hands-on data science with basic data engineering. This is a collaborative, applied role working at the intersection of operations, principal engineering, and global technology and other partners. Develop and apply machine learning and AI methods to predict test outcomes, automate decision-making, and extract insights from complex operational data across UL Solutions laboratories, field engineering, and compliance operations. Work with diverse operational data sources including laboratory information systems, legacy databases, sensor data, and structured/unstructured documents. Collaborate with operations, principal engineering, and technology partners to identify AI opportunities and translate business problems into working solutions. Support the full innovation lifecycle from ideation and feasibility assessment through prototyping, implementation, validation, and production deployment. Evaluate and apply emerging AI techniques -- including large language models, retrieval-augmented generation, and computer vision -- where they add value to operational workflows. Combine scientific methods with business process understanding to ensure solutions are technically sound, operationally relevant, and properly validated. Perform basic data engineering tasks including data extraction, transformation, and pipeline maintenance to ensure reliable data availability for AI and analytics. Support innovation management activities by helping develop blueprints and frameworks to systematically identify, evaluate, classify, and prioritize AI and predictive modeling opportunities - contributing to the team's innovation portfolio from early ideation through feasibility validation and implementation readiness.

Requirements

Minimum of bachelor's degree or equivalent work experience in Data Science, Computer Science, Statistics, Physics, Engineering, or a related quantitative field.
Knowledge of machine learning techniques (clustering, decision trees, random forests, neural networks, regression) and their practical trade-offs.
Python coding skills for data manipulation, analysis, and model development (NumPy, Pandas, Scikit-learn, PyTorch or similar).
Basic understanding of modern AI concepts including large language models, NLP, and retrieval-augmented generation.
Experience querying databases using SQL.
Familiarity with data visualization tools (e.g., Power BI, Matplotlib) to communicate findings.
Basic understanding of data engineering concepts (ETL pipelines, data quality, data integration).
Strong analytical and problem-solving skills with a hands-on, applied mindset and curiosity for emerging technologies.
Good communication and presentation skills - able to explain technical methods and results to non-technical audiences.
Ability to work in a multidisciplinary team and contribute to strategic goals.

Nice To Haves

Exposure to cloud platforms (Azure preferred) and version control (Git/GitHub) is a plus.
Software engineering skills such as test-driven development, CI/CD practices, API design, or containerization are a plus.

Responsibilities

Collect, process, cleanse, and validate data from diverse operational sources for analysis and modeling.
Perform exploratory data analysis on large, complex datasets to identify patterns and determine appropriate analytical techniques.
Build, train, and optimize machine learning models (classification, regression, clustering, time-series) for predictive modeling and other AI use cases.
Explore and prototype AI-driven solutions beyond classical ML, such as NLP, document understanding, or intelligent automation where applicable.
Support feature engineering, model selection, hyperparameter tuning, and model evaluation using established scientific and statistical methods.
Develop and maintain basic ETL workflows and data pipelines to support modeling and reporting needs.
Document model assumptions, limitations, validation results, and alignment with business processes.
Present analysis results, model performance, and recommendations clearly to both technical and non-technical stakeholders.
Stay current with AI and data science developments and assess their applicability to UL Solutions operations.
Contribute to the maturation of data science and AI capabilities across laboratory, field engineering, and compliance operations.