Computational Chemistry and Data Analytics - Academic Graduate Appointee

LLNL•Livermore, CA

2d•$6,748 - $7,718•Hybrid

About The Position

This is a one-year Academic Graduate Appointee position with the possibility of extension to a maximum of two years. The role supports interdisciplinary research in computational chemistry, molecular design, data wrangling, machine learning, and high-performance data analysis by applying established computational methods to defined scientific data-analysis assignments under guidance. The role will assist with developing and maintaining reliable, reproducible workflows for managing and analyzing large-scale chemical, molecular, and biological datasets. Responsibilities include supporting the integration of simulation, experimental, and database data; generating molecular descriptors; contributing to automated ETL pipelines; and creating tools to identify meaningful patterns. The successful candidate will collaborate across computational, experimental, and engineering disciplines to support molecular dynamics, small-molecule inhibitor discovery, AI-enabled drug discovery, and reproducible research methods. Assignments will provide opportunities to develop technical skills and professional experience under the guidance of experienced staff. This position is in the Biochemical and Biophysical Systems Group in the Biosciences and Biotechnology Division within the Physical and Life Sciences Directorate. Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.

Requirements

Ability to secure and maintain a U.S. Department of Energy Q-level security clearance, which requires U.S. citizenship and a federal background investigation.
Bachelor's and/or master's degree in computational chemistry, bioengineering, biomedical engineering, computational biology, bioinformatics, data science, computer science, or a related field.
Coursework, research, internship, or project experience using Linux-based ETL workflows for data ingestion, cleaning, transformation, standardization, and validation of large-scale chemical, molecular, biological, simulation, or experimental datasets.
Familiarity with data-quality checks, validation procedures, and reproducible data-processing workflows particularly those involved with generating, transforming, and analyzing molecular descriptors, chemical features, or other scientific data representations.
Foundational knowledge of one or more of the following: computational chemistry, molecular modeling, molecular dynamics, molecular design, or small-molecule discovery.
Familiarity with, or coursework/project experience in, high-performance, parallel, or distributed data processing systems and practices, including Slurm, Apache Spark, MPI, OpenMP, CUDA, or multi-node computing.
Ability to write clear, maintainable, and well-documented code with proficient verbal and written communication skills necessary to document analyses and present technical information.
Ability to work collaboratively with computational chemists, biologists, experimental scientists, engineers, and other technical personnel on multiple projects, prioritize competing demands, and maintain high-quality standards for deliverables.

Nice To Haves

Experience identifying and addressing data issues part of larger computational ETL workflows, including missing values, inconsistent schemas, duplicate records, incompatible formats, and anomalous observations.
Experience or coursework in website management, development, and optimization including relevant frameworks, packages, protocols, and features.
Experience generating, transforming, curating, or analyzing molecular descriptors, chemical structures, simulation outputs, or biological features through coursework, research, or projects.
Familiarity with workflow automation, batch processing, version control, and documentation for scientific computing environments.
Working knowledge of computational chemistry, molecular modeling, molecular dynamics simulations, molecular design, or small-molecule discovery to collaborate with specialists in the field.

Responsibilities

Collaborate with computational chemists, computational biologists, experimental scientists, computer scientists, and engineers to support defined computational and data analytics assignments under guidance.
Assist in implementing and maintaining data-wrangling workflows to collect, ingest, clean, transform, standardize, and validate chemical, molecular, simulation, and biological datasets using established methods.
Contribute to the development and testing of ETL pipelines for high-volume scientific data, including molecular structures, simulation outputs, and molecular descriptors.
Assist with data from computational databases, molecular dynamics simulations, high-throughput experiments, and external sources.
Identify data-quality issues, including missing, duplicated, inconsistent, incomplete, or anomalous data using established data-quality practices and escalate nonroutine issues to senior team members.
Develop basic data visualizations and analysis tools to identify chemical, structural, and biological trends while maintaining well-documented, version-controlled, and reproducible code for scientific data analysis.
Present research progress and technical results to internal team members and, as appropriate, collaborators, and other scientific audiences with guidance from senior staff while maintaining high-quality deliverables.
Meet all respective deadlines.
Perform other duties as assigned.