Principal Exposure Data Scientist - Chemical Insights Research Institute
Lucas James Talent Partners, Morrisville, NC, United States
Principal Exposure Data Scientist - Chemical Insights Research Institute
Full time | Lucas James Talent Partners | United States
Posted On 04/24/2026
Job Information
Nonprofit Charitable Organizations
City Morrisville
State/Province North Carolina
27560
Job Description
Lucas James Talent Partners is recruiting on behalf of UL Research Institutes. We have an exciting opportunity for a Principal Exposure Data Scientist at UL Research Institutes, based in our Morrisville, NC office.
The Principal Exposure Data Scientist will assist in the development and maintenance of the data analysis infrastructure to support the Chemical Insights Research Institute (CIRI) mission of advancing human and environmental health. This role focuses on developing data analysis pipelines, ensuring efficient data exchange and interoperability across diverse platforms and scientific disciplines, and applying advanced machine learning/AI methods and statistical frameworks to identify and quantify trends or patterns in complex, large‑scale datasets. The Principal Exposure Data Scientist works closely with CIRI scientists and external collaborators to deliver high‑quality scientific data that inform chemical exposure assessment, risk assessment, regulatory decisions, and public health guidance.
What you’ll learn and achieve:
Design and implement cutting‑edge data science methods for chemical exposure.
Work as part of a team to extract, curate, and harmonize structured and unstructured chemical exposure, product ingredient, biomonitoring, and environmental contamination data.
Develop and implement quality assurance plans for data curation projects.
Design and implement artificial intelligence and machine learning solutions to automate data extraction, curation, and quality evaluation of structured and unstructured data.
Develop and implement data mapping and extraction, transformation, and load (ETL) pipelines for efficient exchange of data between established chemical safety and exposure data systems (e.g., IUCLID, MMDB, CPDat).
Develop statistical and machine learning models to predict chemical functional use and exposure pathways.
Collaborate with exposure scientists, toxicologists, analytical chemists, and toxicokinetic scientists to provide solutions for linking cross‑disciplinary data, computational modeling, and interpreting experimental results.
Work closely with software and database engineers to provide high‑quality chemical exposure data for online software applications and decision support tools.
Effectively communicate complex technical concepts, methodologies, and results to diverse audiences, including senior management, amplification partners, and data stakeholders.
Stay up‑to‑date with the latest research and advancements in data science, machine learning, and artificial intelligence, and contribute to the development of new methodologies and best practices.
Present research findings at scientific conferences, stakeholder meetings, and technical forums.
Serve as co‑author on peer‑reviewed publications and technical reports.
Assist in writing research proposals and securing funding from internal and external sources.
Provide technical support and troubleshooting for data‑related issues.
Perform other duties as assigned.
Benefits
Bonus compensation.
Comprehensive medical, dental, vision, and life insurance plans.
Generous 401(k) matching structure of up to 5% of eligible pay, with an additional 4% contributed to your retirement savings after the first year of continuous employment.
Flexible working arrangements may be discussed with your manager.
Paid time off, including vacation, holiday, sick, and volunteer days.
What makes you a great fit:
Proficiency in programming and statistical languages (e.g., Python, Java, R).
Knowledge of machine learning, statistical modeling, and data visualization tools.
Working knowledge of exposure science, chemistry, and toxicokinetics.
Understanding of relational and non‑relational database systems.
Proven ability to participate in multidisciplinary teams on complex projects in a research setting.
Excellent problem‑solving and analytical capabilities, with the ability to adapt to new challenges and prioritize competing demands.
Willingness to learn and research new concepts and technologies.
Ability to communicate with technical and non‑technical internal and external stakeholders.
Skilled in versioning best practices, i.e., GitHub, Code Commit.
Professional education and experience requirements for the role include:
Master’s Degree in Environmental Health, Data Science, Chemistry, or Chemical Engineering and at least 6 years of relevant experience; or
Doctoral Degree in Environmental Health, Data Science, Chemistry, or Chemical Engineering and at least 4 years of relevant experience.
Solid technical knowledge and experience working with R, Java or Python programming language, relational (SQL and Postgres) databases.
Demonstrated experience in developing data extraction and curation workflows for structured and unstructured chemical exposure data in a research environment.
Prior experience in developing quantitative structure‑property relationship models to predict chemical functional use and exposure pathways.
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Full time | Lucas James Talent Partners | United States
Posted On 04/24/2026
Job Information
Nonprofit Charitable Organizations
City Morrisville
State/Province North Carolina
27560
Job Description
Lucas James Talent Partners is recruiting on behalf of UL Research Institutes. We have an exciting opportunity for a Principal Exposure Data Scientist at UL Research Institutes, based in our Morrisville, NC office.
The Principal Exposure Data Scientist will assist in the development and maintenance of the data analysis infrastructure to support the Chemical Insights Research Institute (CIRI) mission of advancing human and environmental health. This role focuses on developing data analysis pipelines, ensuring efficient data exchange and interoperability across diverse platforms and scientific disciplines, and applying advanced machine learning/AI methods and statistical frameworks to identify and quantify trends or patterns in complex, large‑scale datasets. The Principal Exposure Data Scientist works closely with CIRI scientists and external collaborators to deliver high‑quality scientific data that inform chemical exposure assessment, risk assessment, regulatory decisions, and public health guidance.
What you’ll learn and achieve:
Design and implement cutting‑edge data science methods for chemical exposure.
Work as part of a team to extract, curate, and harmonize structured and unstructured chemical exposure, product ingredient, biomonitoring, and environmental contamination data.
Develop and implement quality assurance plans for data curation projects.
Design and implement artificial intelligence and machine learning solutions to automate data extraction, curation, and quality evaluation of structured and unstructured data.
Develop and implement data mapping and extraction, transformation, and load (ETL) pipelines for efficient exchange of data between established chemical safety and exposure data systems (e.g., IUCLID, MMDB, CPDat).
Develop statistical and machine learning models to predict chemical functional use and exposure pathways.
Collaborate with exposure scientists, toxicologists, analytical chemists, and toxicokinetic scientists to provide solutions for linking cross‑disciplinary data, computational modeling, and interpreting experimental results.
Work closely with software and database engineers to provide high‑quality chemical exposure data for online software applications and decision support tools.
Effectively communicate complex technical concepts, methodologies, and results to diverse audiences, including senior management, amplification partners, and data stakeholders.
Stay up‑to‑date with the latest research and advancements in data science, machine learning, and artificial intelligence, and contribute to the development of new methodologies and best practices.
Present research findings at scientific conferences, stakeholder meetings, and technical forums.
Serve as co‑author on peer‑reviewed publications and technical reports.
Assist in writing research proposals and securing funding from internal and external sources.
Provide technical support and troubleshooting for data‑related issues.
Perform other duties as assigned.
Benefits
Bonus compensation.
Comprehensive medical, dental, vision, and life insurance plans.
Generous 401(k) matching structure of up to 5% of eligible pay, with an additional 4% contributed to your retirement savings after the first year of continuous employment.
Flexible working arrangements may be discussed with your manager.
Paid time off, including vacation, holiday, sick, and volunteer days.
What makes you a great fit:
Proficiency in programming and statistical languages (e.g., Python, Java, R).
Knowledge of machine learning, statistical modeling, and data visualization tools.
Working knowledge of exposure science, chemistry, and toxicokinetics.
Understanding of relational and non‑relational database systems.
Proven ability to participate in multidisciplinary teams on complex projects in a research setting.
Excellent problem‑solving and analytical capabilities, with the ability to adapt to new challenges and prioritize competing demands.
Willingness to learn and research new concepts and technologies.
Ability to communicate with technical and non‑technical internal and external stakeholders.
Skilled in versioning best practices, i.e., GitHub, Code Commit.
Professional education and experience requirements for the role include:
Master’s Degree in Environmental Health, Data Science, Chemistry, or Chemical Engineering and at least 6 years of relevant experience; or
Doctoral Degree in Environmental Health, Data Science, Chemistry, or Chemical Engineering and at least 4 years of relevant experience.
Solid technical knowledge and experience working with R, Java or Python programming language, relational (SQL and Postgres) databases.
Demonstrated experience in developing data extraction and curation workflows for structured and unstructured chemical exposure data in a research environment.
Prior experience in developing quantitative structure‑property relationship models to predict chemical functional use and exposure pathways.
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