Assistant Computational Data Scientist

Argonne National Laboratory is a Department of Energy national laboratory with over 1,500 scientists and engineers performing world-class research aimed at solving the nation's energy, environmental and security challenges.  Argonne is located 25 miles southwest of Chicago on a beautiful 1,500-acre campus. 

 

The Decision and Infrastructure Sciences (DIS) division researches, designs, and delivers scientific tools and methodologies to inform decision-making regarding the most challenging security, risk, and resilience problems. As a multidisciplinary national laboratory, Argonne offers an exciting atmosphere in which to collaborate on interdisciplinary projects that focus on complex technical problems and integrated application of natural, social, and computer science practices. 

To assist our research mission, DIS is seeking a Computational Scientist to strengthen current and future applications of artificial intelligence (AI), machine learning (ML), agent-based modeling, and related modeling and simulation capabilities across the Division’s research portfolio, including topics related to infrastructure security and resilience, energy security, economic security, and emergency management. Successful candidates should be familiar with performing advanced data analysis across different technical domains; developing predictive models, simulations, and optimization algorithms; and collaborating with multi-disciplinary teams to identify and address key operational challenges.

This role includes statistical analysis using a variety of modeling techniques, developing prototype solutions using scientific programming languages, designing experiments for validation and testing efficacy of models, and measuring impact. The role involves identifying concepts and developing procedures for investigating problems of a significant nature, which may be broadly stated or ill-defined.

 

In this position, you will:

• Contribute to projects associated with AI/ML, high-performance computing (HPC), and computational problems related to the Division’s core missions and sponsors.

• Develop novel methodologies and contribute to emerging technical approaches that apply AI/ML and agent-based modeling to sponsored research that informs decision-making across a broad range of industry and community stakeholders.

• Work with an interdisciplinary team of domain scientists (e.g., decision science, computational scientists, engineers, infrastructure analysts, etc.) to develop decision-ready toolsets based upon AI/ML approaches.

• Contribute to projects associated with Large Language Model (LLM) toolset development for decision science applications among industry and community stakeholders.

• Support engagements with DOE and other federal entities, State, Local, Tribal, and Territorial governments, and industry sponsors to inform the development of AI/ML tools that best align with stakeholder decision needs.

• Interface with subject matter experts in key fields from across Argonne to produce decision-relevant AI/ML resources across a range of scientific domains in the DIS sponsored research portfolio.

• Work with DIS and other Argonne subject matter experts to support government sponsors interested in a wide range of both domestic and international AI/ML-informed decision-support.

Open to the following work locations:

• Hybrid work schedules for individuals in the Chicago, IL region, or

• Remote with travel expectations between 10-15% dependent on sponsor needs.

Position Requirements

• Minimum Education/Experience Requirement: Bachelor's and 0+ years, Master's and 0+ years or equivalent

• Experience performing AI/ML-related activities, including data identification, cleansing and preparation, algorithm development, statistical learning, and validation of results.

• Experience working with generative AI, natural language processing, network modeling, and AI assistant models is preferred.

• Knowledge and experience with coding languages including Python, MatLab, SQL, Java, and Julia.

• Knowledge and experience with machine learning libraries including TensorFlow, PyTorch, Scikit-learn, Pandas, and Numpy.

• Knowledge and experience with modeling and simulation, including agent-based modeling, Monte Carlo, and life cycle assessment.

• Ability to use independent judgment and abstract from specific problems to general solutions/findings.

• Strong oral and written communications skills, including the ability to translate technical information into plain language, and strong ability to work effectively in a team-based project environment.

• Skill and experience working on multiple projects simultaneously, including sound project management practices and the ability to manage deadlines effectively.

• Skill and experience collaborating with team members and leading independent analysis in support of projects.

• A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Job Family

Research Development (RD)

Job Profile

Computational Science 1

Worker Type

Regular

Time Type

Full time

The expected hiring range for this position is $74,799.00 - $118,785.42.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.