Environmental Data Scientist

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AECOM is seeking a dynamic Environmental Data Scientist to support PFAS chemistry consulting services and business development across the US West Region. This consulting position will primarily support PFAS-focused projects and initiatives within our Environment business line, with broad exposure to cross-disciplinary projects in other subjects and business lines. This position is expected to begin in November 2026. Job Description: AECOM is actively seeking a creative, highly talented PFAS Environmental Data Scientist for immediate employment as a hybrid in-office employee in Raleigh, NC. Please note this is an office position and would not involve routine laboratory analysis, though there may be opportunities for onsite field work and laboratory visits. The data scientist will support PFAS site investigation and remediation projects through contributions to experimental and sampling design, data management, quality assurance/quality control (QA/QC), statistical and geospatial analysis, and conceptual site model (CSM) development. More broadly, the role will also help build AECOM’s Environmental AI/ML capabilities, including AI-enabled tools for environmental data analysis and large-scale, curated environmental data libraries used to train, validate, and deploy ML models across projects. The candidate will have the opportunity to work on a wide range of projects across multiple business lines and geographies while developing their technical skills and exploring new interests. This role is integral to the growth of PFAS chemistry consulting services, building expertise in advanced analytical tools for PFAS forensics and characterization to address the broad variety of challenges posed by PFAS. Job responsibilities include, but may not be limited to: Provide defensible scientific support for critical project decisions, especially related to PFAS source identification and apportionment, CSMs, and multivariate data analysis Design, develop, validate, and deploy machine learning, statistical, and generative AI applications for environmental science and engineering Build and maintain large-scale, curated libraries of environmental data (analytical, geospatial, hydrogeologic, and unstructured document data) with standardized schemas, metadata, data lineage, and QA/QC to support ML model training, validation, and reuse across projects Develop and maintain extract, transform, and load (ETL) pipelines and relational databases that ensure the quality, integrity, and traceability of environmental data Design and perform statistical tests to support anthropogenic background studies for PFAS and other constituents Engage with commercial laboratories and innovators in PFAS sensing to coordinate and advance innovative data applications Engage with a broad range of practitioners in industry and government to maintain currency in environmental analytical methodologies and data interpretation practices Support data evaluation, including regulatory comparison, and associated reporting for field investigation and remediation reports Aid in the data-driven experimental design and data evaluation for forensic fingerprinting, remediation decisions, custom bench-scale studies, and environmental fate and transport studies Develop project-specific data quality metrics and best practices for work plans, sampling design, sampling and analysis plans (SAPs), and quality assurance project plans (QAPPs) Solicit analytical laboratory services and evaluate electronic data deliverable (EDD) capabilities for project use Prepare technical memoranda and white papers to be included in client deliverables Facilitate collaboration within AECOM, across business lines and global geographies, and aid in developing relationships with external partners, including academic and commercial laboratories on a global scale Support AECOM’s global PFAS technical practitioners and academic or commercial partners in delivering meaningful research to address PFAS fate and transport, treatment, and risk related challenges. Identify, prototype, and help scale AI/ML-enhanced workflows with project teams Automate reporting deliverables, data validation, and data management workflows (e.g., Python, SQL, Power BI, EQuIS) Minimum Requirements: Bachelor’s degree in Data Science, Statistics, Computer Science, Environmental Science or Engineering, Chemistry, Geology, or a related quantitative field and 4+ years of relevant experience in environmental data analysis, data management, or environmental chemistry, or demonstrated equivalency of experience and/or education Preferred Qualifications Experience applying multivariate statistical analyses and/or machine learning techniques to PFAS forensics and source apportionment Working knowledge of PFAS chemistry, including sources and use history, nomenclature, analytical methods (e.g., EPA Method 1633, EPA Method 537.1, total oxidizable precursor [TOP] assay, non-targeted analysis), and fate, transport, and precursor transformation processes Demonst