... remote sensing products, model simulations) to inform model development, calibration, and ... Knowledge of uncertainty quantification methods and causal inference for complex environmental ...
... remote sensing products, model simulations) to inform model development, calibration, and ... Knowledge of uncertainty quantification methods and causal inference for complex environmental ...
Remote Causal Inference information
What is a Remote Causal Inference job?
A Remote Causal Inference job involves using statistical and analytical methods to determine cause-and-effect relationships from data, often for fields like healthcare, social sciences, or business. Professionals in this role work remotely, leveraging tools such as R, Python, or specialized software to analyze experiments, observational studies, or large datasets. Their insights help organizations make data-driven decisions, design better interventions, and accurately measure the impact of policies or treatments. Strong skills in statistics, machine learning, and communication are essential for success in this position.
What are the key skills and qualifications needed to thrive as a Remote Causal Inference Specialist, and why are they important?
To thrive as a Remote Causal Inference Specialist, you need strong quantitative and statistical skills, a solid background in econometrics or data science, and typically an advanced degree in a related field. Proficiency with statistical programming languages such as R or Python, experience with causal inference frameworks like propensity score matching or instrumental variables, and familiarity with data visualization tools are crucial. Outstanding problem-solving abilities, clear communication, and self-motivation are essential soft skills for working independently and conveying complex results to non-technical stakeholders. These skills enable accurate, actionable insights from data, which drive evidence-based decision-making in remote, collaborative environments.
How does a remote Causal Inference specialist typically collaborate with cross-functional teams, and what tools are commonly used?
As a remote Causal Inference specialist, you’ll frequently work with data scientists, product managers, and engineers to design and interpret experiments, analyze observational data, and provide actionable insights. Collaboration usually happens through regular video meetings, shared documentation, and project management tools. Commonly used platforms include Slack or Microsoft Teams for communication, GitHub for code collaboration, and Jupyter Notebooks or RMarkdown for sharing reproducible analyses. These tools help ensure transparency and maintain strong teamwork despite the remote environment.
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What are popular job titles related to Remote Causal Inference jobs in Tennessee? For Remote Causal Inference jobs in Tennessee, the most frequently searched job titles are:
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Postdoctoral Research Associate - AI for Hydrological Modeling
Oak Ridge, TN • On-site, Remote
Full-time
Medical, Dental, Retirement, PTO
Re-posted 15 days ago
Oak Ridge National Laboratory rating
8.8
Based on 16 frontline employees who took The Breakroom Quiz
10th of 105 rated laboratories
Job description
Requisition Id 16757
Overview:
The Computational Hydrology and Atmospheric Science (CHAS) Group at Oak Ridge National Laboratory (ORNL) is seeking a highly motivated Postdoctoral Research Associate in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and data-model integration, leveraging the U.S. Department of Energy’s (DOE) Leadership-Class Computing Facilities to advance predictive understanding of complex environmental systems.
Major Duties/Responsibilities:
- Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions.
- Design and implement physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction.
- Conduct multimodal, multiscale data analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation.
- Collaborate with a multidisciplinary team of hydrologists, Earth scientists, and computational scientists to leverage leadership-class computing resources for large-scale model training, testing, and deployment.
- Contribute to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery.
- Publish research findings in high-impact journals and present results at national and international conferences.
- Engage with collaborators across DOE laboratories, universities, and partner agencies to broaden the applications of AI-enabled hydrological modeling.
- Ensure compliance with ORNL’s safety, security, quality, and environmental standards while carrying out all research activities.
Technical Questions:
Please contact Dan Lu lud1@ornl.gov
Basic Qualifications:
- A Ph.D. in Hydrology, Earth system science, Water resources engineering, Computational sciences, Computer sciences or a related field completed within the last 5 years (or expected soon).
- Demonstrated experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction.
- Strong background in computational sciences, including numerical methods, high-performance computing (HPC), or large-scale data analysis.
- Experience in applying AI/ML techniques to hydrological and Earth sciences.
- Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C++.
- Evidence of scholarly productivity, including peer-reviewed publications and conference presentations.
- Excellent written and oral communication skills and the ability to work effectively in a collaborative, multidisciplinary team environment.
Preferred Qualifications:
- Knowledge of uncertainty quantification methods and causal inference for complex environmental systems.
- Experience with large-scale Earth system simulations, particularly using the Energy Exascale Earth System Model (E3SM).
- Background in coastal and compound flooding simulations, including subsurface–surface and hydrodynamic interactions.
- Demonstrated ability and strong motivation to conduct innovative, high-impact research and disseminate results through peer-reviewed publications and conference presentations.
Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.
ORNL Ethics and Conduct:
As a member of the ORNL scientific community, you will be expected to commit to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity
Benefits at ORNL:
UT Battelle offers an exceptional benefits package to include matching 401K, Pension Plan, Paid Vacation and Medical / Dental plan. Onsite amenities include Credit Union, Medical Clinic and free Fitness facilities.
Relocation:
UT Battelle offers a wide range of relocation benefits for individuals and families to make it easier to come and work here. If you are invited to interview, please ask your Recruiter about relocating with ORNL
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.
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About Oak Ridge National Laboratory
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Industry
Scientific research and development services
Company size
5,001 - 10,000 Employees
Headquarters location
Oak Ridge, TN, US
Year founded
1943