1

Bayesian Modeling Jobs in South Carolina (NOW HIRING)

$95.30K - $125.50K/yr

Statistical inference (Frequentist and Bayesian methods) * Data processing and normalization techniques * Experience building and training models on real-world datasets (not just academic exercises)

Be Seen First

AI Lead Engineer

Charleston, SC · On-site

$125K - $145K/yr

Statistical inference (Frequentist and Bayesian methods) * Data processing and normalization techniques * Experience building and training models on real-world datasets (not just academic exercises)

Bayesian Modeling information

What are the key skills and qualifications needed to thrive as a Bayesian Modeler, and why are they important?

To thrive as a Bayesian Modeler, you need a solid background in statistics, probability theory, and mathematical modeling, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as R, Python, or Stan, and experience with statistical software and Bayesian inference tools are essential. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting results and collaborating with multidisciplinary teams. These skills ensure accurate model development, reliable data-driven insights, and clear communication of complex findings to stakeholders.

How does a Bayesian Modeling specialist typically collaborate with cross-functional teams in a workplace setting?

Bayesian Modeling specialists often work closely with data scientists, software engineers, and domain experts to integrate probabilistic models into larger analytical or production systems. They are involved in translating complex statistical concepts into actionable insights and recommendations tailored to business needs. Effective communication is key, as they must present findings to both technical and non-technical stakeholders, ensuring that model assumptions and results are clearly understood. Collaboration may also include contributing to code reviews, sharing best practices for model validation, and mentoring colleagues on Bayesian methodologies.

What is Bayesian modeling?

Bayesian modeling is a statistical approach that uses Bayes' Theorem to update the probability of a hypothesis as more data becomes available. It incorporates prior beliefs or knowledge, combines them with observed data, and produces a posterior probability distribution to guide inference and decision-making. This approach is widely used in various fields such as machine learning, data science, and scientific research for tasks like parameter estimation, prediction, and model selection.

What is the difference between Bayesian Modeling vs Data Scientist?

AspectBayesian ModelingData Scientist
Required CredentialsStatistics, Mathematics, Data AnalysisStatistics, Computer Science, Data Analysis
Work EnvironmentResearch-focused, statistical modelingCross-functional, data analysis, visualization
Industry UsageResearch, academia, specialized analyticsBusiness, tech, finance, healthcare
Common Search/ComparisonYesYes

Bayesian Modeling and Data Scientists often overlap in skills like statistics and data analysis. Bayesian Modeling specializes in probabilistic models and statistical inference, while Data Scientists have broader roles including data cleaning, visualization, and machine learning. Both roles are essential in data-driven industries, but Bayesian Modeling is more focused on advanced statistical techniques.

What are popular job titles related to Bayesian Modeling jobs in South Carolina? For Bayesian Modeling jobs in South Carolina, the most frequently searched job titles are:
What job categories do people searching Bayesian Modeling jobs in South Carolina look for? The top searched job categories for Bayesian Modeling jobs in South Carolina are:
Subsurface Reactive Fate & Transport Modeling Scientist

Subsurface Reactive Fate & Transport Modeling Scientist

Savannah River National Laboratory

Aiken, SC • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 18 days ago


Job description

Job Description
Savannah River National Laboratory (SRNL) is seeking a midcareer scientist with strong expertise in subsurface reactive fate and transport modeling to support research and mission-driven applications focused on environmental remediation, groundwater protection, and long-term stewardship at the Savannah River Site (SRS) and other DOE-managed lands.
The successful candidate will develop, apply, and communicate advanced numerical models to predict the coupled hydrologic, geochemical, and biological processes governing contaminant behavior in complex subsurface environments. This role supports SRNL's mission to deliver credible science-based solutions that ensure the protection of human health and the environment.
The position involves technical leadership, collaboration across multidisciplinary teams, mentoring of early-career staff, and direct interaction with DOE customers and regulatory stakeholders.
Responsibilities
Technical Modeling & Analysis
  • Develop and apply reactive transport models (e.g., PFLOTRAN, TOUGHREACT, CrunchFlow/CrunchTope, PHREEQC, HYDRUS, COMSOL, FEFLOW) to evaluate contaminant fate, plume evolution, and remedial strategy performance.
  • Integrate aqueous geochemistry, sorption/kinetics, mineral reactions, and redox transformations into model frameworks.
  • Conduct model calibration, validation, sensitivity analysis, and uncertainty quantification using tools such as PEST/PEST++, DAKOTA, or Bayesian methods.
  • Develop and refine conceptual site models using hydrogeologic, geochemical, mineralogical, and field performance data.

Data Integration & Interpretation
  • Assimilate field monitoring, laboratory testing, geospatial data, and historical site knowledge into modeling workflows.
  • Generate reduced-order models, computational surrogates, or decision-support tools to inform risk assessments and remediation planning.
  • Produce high-quality visualizations, technical interpretations, and defensible documentation suitable for DOE and regulatory review.

Program and Project Support
  • Serve as a technical lead or task manager for modeling efforts within multi-institutional or multi-disciplinary projects.
  • Contribute to proposal development, R&D planning, and engagement with DOE program managers, site operations personnel, and regulators.
  • Ensure all deliverables meet SRNL's standards for quality, safety, and regulatory compliance.

Collaboration & Mentorship
  • Mentor junior scientists, modelers, and interns on modeling techniques, scientific writing, and workflow reproducibility.
  • Work closely with SRNL hydrologists, geochemists, microbial scientists, field engineers, and data scientists to design studies and integrate models with experimental work.

Competencies
  • Strong technical judgment and ability to design rigorous, defensible modeling studies.
  • Effective communicator, able to translate complex technical outputs into clear, actionable insights.
  • Demonstrated ability to work effectively in multidisciplinary teams and build strong collaborations across SRNL, SRS operations, and partner organizations.
  • Commitment to safety, quality, and scientific integrity.

Qualifications
Minimum Qualifications:
  • Ph.D. in Hydrogeology, Geosciences, Environmental Engineering, Chemical Engineering, Civil Engineering, or related field with 6-8+ years of relevant experience; or M.S. with 8-10 years of relevant experience.
  • Ability to obtain and maintain security clearance; U.S. citizenship is legally required.
  • Demonstrated experience developing and calibrating reactive fate and transport models for groundwater or vadose-zone systems.
  • Strong foundation in aqueous geochemistry and subsurface flow and transport processes.
  • Proficiency in Python (preferred), MATLAB, or R for model pre- and post-processing.
  • Experience preparing technical reports and communicating results to technical and non-technical audiences.

Preferred Qualifications:
  • Experience with HPC environments, parallel computing, containers, or cloud-ready workflows.
  • Background in geostatistics, stochastic modeling, or Bayesian methods.
  • Experience with contaminants relevant to DOE legacy sites (e.g., uranium, technetium, strontium, cesium, nitrate, chlorinated solvents, PFAS).
  • Familiarity with DOE Orders, CERCLA, and other environmental regulatory frameworks.
  • Experience with reduced-order modeling, machine learning integration, or physics-informed ML methods.
  • Experience supporting or leading DOE-funded R&D programs.
  • Knowledge of GIS tools (ArcGIS/QGIS), geospatial analysis, and database management.

Work Environment & Additional Requirements
  • Position is based at SRNL within the Savannah River Site; hybrid work arrangements may be considered.
  • Occasional travel to project sites, conferences, or sponsor meetings (up to 10-15%).

About Us
"We put science to work!"
Savannah River National Laboratory (SRNL) is a multi-program laboratory applying state of the art science and practical, high-value, cost-effective solutions to complex technical problems to protect the nation. Located at the U.S. Department of Energy's (DOE) Savannah River Site (SRS) in Aiken SC, the laboratory develops and deploys innovative technologies to address some of the nation's environmental, energy, and national security challenges.
Battelle Savannah River Alliance (BSRA) is constantly assessing trends to provide the best possible benefits to our workforce. We also negotiate cost effective premiums that will meet the needs of our evolving workforce.
Some of the *Benefits offered to employees include:
*Benefits vary based upon employment status
  • Highly competitive Medical, Dental, and Vision options including HSA options with company provided seed
  • Short- & Long-Term Disability (company paid)
  • Life Insurance Non-Contributary 1X salary (company paid)
  • AD&D Non-contributary 1x salary (company paid)
  • Savings & Investment plan:
    • Qualified Non-Elective Company Contribution of 5% each pay period with immediate vesting
    • Company match 50 cents/dollar up to 8% (5 yrs. vesting in company match)
  • Contributory Life Insurance up to 5x Salary with $1M Cap
  • Contributory AD&D (employee, spouse and children)
  • Paid Time Off
  • Employee Assistance Plan
  • SRNL offers a competitive relocation package to ease the transition process. Domestic and international relocation assistance is available for certain positions.

For more information about our benefits, working here, and living here, visit the "About" tab at www.srnl.doe.gov.
BSRA is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or protected veteran status. BSRA is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. Please email us at SRNLRecruiting@srnl.doe.gov with any questions regarding the hiring process or to request an accommodation.
About the Team
SRNL's Environmental & Legacy Management (ELM) Directorate closely collaborates with the Department of Energy (DOE) and site contractors to develop, mature, and apply science and technology needed to resolve environmental challenges and advance legacy management missions. As the lead laboratory for Environmental Management (DOE-EM) and Legacy Management (DOE-LM), SRNL applies our talent and expertise to develop and deploy innovative approaches and technologies to reduce risk, cost, and schedule of environmental cleanup and nuclear material processing. Additionally, ELM is using our competencies to develop new materials and processes for a range of clean energy applications.