Postdoctoral Appointee - Supply Chain Risk Analyst

Key Responsibilities

• Spearhead the research and development of predictive models and strategic tools designed to support decision making in strengthening both domestic and international supply chains, particularly focusing on energy technologies.
• Collaborate on the creation of a comprehensive supply chain database specifically for targeted energy technologies.
• Apply advanced analytics to assess and classify domestic and global sourcing strategies, taking into consideration of various risks and uncertainties.
• Partner with an interdisciplinary team to support creation of supply chain databases and develop user-friendly software interfaces that enhance data accessibility of data and insight for diverse stakeholders.
• Facilitate ongoing communications and foster relationships with a broad array of stakeholders, including community groups, governmental bodies, and private sector entities.
• Conduct rigorous analysis and develop models for improving the domestic and global resilience of supply chains for energy technologies
• Support stakeholder engagement and community building activities such as townhalls, info sessions and workshops
• Communicate research outcomes through scientific and technical reports, peer-reviewed publications, conference papers and presentations

Position Requirements

• This level of knowledge is typically achieved through a formal education in Statistics, Machine Learning, Computer Science, Logistics/Supply Chain, or a related field at the Ph.D. level with zero to five years of employment experience.
• Demonstrated experience in leading research initiatives, with a strong track record of publishing in peer-reviewed journals.
• Excellent communication skills, capable of crafting and presenting complex information effectively to a variety of audiences.
• Ability to work collaboratively in a multidisciplinary team setting.
• Commitment to Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Desired Knowledge, Skills, And Experience

• Proven capability in convergent thinking and systems analysis, drawing on diverse academic backgrounds such as engineering, computer science, statistics, and economics.
• Familiarity with energy technologies and associated supply chain risk and challenges.
• Familiarity with the application of statistics and machine learning techniques in analyzing supply chain risk.
• Strong programming skills in statistical languages such as R or Python.