Lead Exposure Data Scientist

About the role

The Lead Exposure Data Scientist will be responsible for assisting in the development and maintenance of the data analysis infrastructure to support Chemical Insights' 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.

Responsibilities

• 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.
• Develop 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 on-line 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.
• Absorb and assist in writing research proposals and securing funding from internal and external sources.
• Provide technical support and troubleshooting for data-related issues.

Requirements

• Master’s Degree in Environmental Health, Data Science, Chemistry, or Chemical Engineering and at least 5 years of relevant experience; or Doctoral Degree in Environmental Health, Data Science, Chemistry, or Chemical Engineering and at least 3 years of relevant experience.
• Proven experience in developing data extraction and curation workflows for structured and unstructured chemical exposure data in a research environment.
• Strong proficiency in programming and statistical languages (e.g. Python, Java, R).
• Working knowledge of exposure science, chemistry, and toxicokinetics.
• Demonstrated experience in relational and non-relational database systems.
• Proven ability to participate in multidisciplinary teams on complex projects in a research setting.
• Demonstrated 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 stakeholders.
• Skilled in versioning best practices, i.e. GitHub, Code Commit.