Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Quick apply
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Nuclear Engineering Data Scientist
Lynchburg, VA · Hybrid
$86K - $136K/yr
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Nuclear Engineering Data Scientist
Lynchburg, VA · Hybrid
$86K - $136K/yr
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
Nuclear Engineering Data Scientist
Lynchburg, VA · Hybrid
$86K - $136K/yr
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
Nuclear Engineering Data Scientist
Lynchburg, VA · Hybrid
$86K - $136K/yr
Implement uncertainty quantification techniques, such as ensembling and Bayesian methods, to ensure robust and reliable predictions in nuclear systems. * Optimize nuclear engineering processes using ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
... uncertainty quantification, and data engineering. You will work closely with other groups to support the missions of the Laboratory. You will work closely with multidisciplinary teams and ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Quick apply
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
... uncertainty quantification and data-quality metrics to prioritize measurements and avoid low-value or unreliable regions. Qualifications : Required : • Successfully complete a minimum of 6 graduate ...
... uncertainty quantification and data-quality metrics to prioritize measurements and avoid low-value or unreliable regions. Qualifications : Required : • Successfully complete a minimum of 6 graduate ...
Senior Engineer, Radar Modeling & Simulation (Dallas/San Diego) (R4537)
San Diego, CA · On-site
$105K - $200K/yr
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Senior Engineer, Radar Modeling & Simulation (Dallas/San Diego) (R4537)
San Diego, CA · On-site
$105K - $200K/yr
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Senior Scientist, AI Applications - R&D Oncology
Cambridge, MA · On-site
$100.80K - $137.70K/yr
... uncertainty quantification and reliability assessment in AI-driven scientific reasoning • Serve as a domain expert within the field by articulating to team members emerging approaches and ...
Senior Scientist, AI Applications - R&D Oncology
Cambridge, MA · On-site
$100.80K - $137.70K/yr
... uncertainty quantification and reliability assessment in AI-driven scientific reasoning • Serve as a domain expert within the field by articulating to team members emerging approaches and ...
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Quick apply
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning-based surrogates) to push the state of the art. * Document assumptions, equations ...
Uncertainty Quantification information
See salary details
$30K - $38.3K
11% of jobs
$45.7K is the 25th percentile. Wages below this are outliers.
$38.3K - $46.5K
16% of jobs
$46.5K - $54.8K
11% of jobs
The median wage is $61K / yr.
$54.8K - $63.1K
17% of jobs
$63.1K - $71.4K
9% of jobs
$79.3K is the 75th percentile. Wages above this are outliers.
$71.4K - $79.6K
13% of jobs
$79.6K - $87.9K
10% of jobs
$87.9K - $96.2K
3% of jobs
$96.2K - $104.5K
3% of jobs
$104.5K - $112.7K
5% of jobs
$112.7K - $121K
3% of jobs
$30K
$69.5K
$121K
How much do uncertainty quantification jobs pay per year?
As of Jun 1, 2026, the average yearly pay for uncertainty quantification in the United States is $69,454.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,000.00 and $83,000.00 per year, depending on experience, location, and employer.
What are the key skills and qualifications needed to thrive as an Uncertainty Quantification Specialist, and why are they important?
To thrive as an Uncertainty Quantification Specialist, you need a strong background in applied mathematics, statistics, and computational modeling, often supported by an advanced degree in a quantitative field. Familiarity with programming languages such as Python or MATLAB, and experience with simulation tools and statistical analysis software, are typically required. Strong problem-solving skills, attention to detail, and effective communication help professionals convey complex concepts to interdisciplinary teams. These skills are crucial for accurately assessing risks, making data-driven decisions, and improving the reliability of models in engineering, finance, or scientific research.
What are some common challenges faced by professionals in Uncertainty Quantification when working on multidisciplinary teams?
Professionals in Uncertainty Quantification (UQ) often collaborate with experts from fields like engineering, data science, and physics. A common challenge is communicating complex statistical concepts in an accessible way to team members without a quantitative background. Additionally, integrating uncertainty models into existing workflows and ensuring that all stakeholders understand how uncertainty impacts decision-making can be demanding. Effective UQ professionals are proactive in facilitating clear communication and tailoring their approach to fit the needs of diverse teams.
What is Uncertainty Quantification?
Uncertainty Quantification (UQ) is the science of quantifying, managing, and reducing uncertainties in computational models and real-world systems. It involves identifying sources of uncertainty in data, model parameters, and algorithms, then using statistical and mathematical methods to assess their impact on model predictions. UQ is essential in fields like engineering, finance, and environmental science to ensure that predictions and decisions are robust and reliable. Practitioners use techniques such as sensitivity analysis, probabilistic modeling, and Monte Carlo simulations to quantify and analyze uncertainties.
What is the difference between Uncertainty Quantification vs Data Scientist?
| Aspect | Uncertainty Quantification | Data Scientist |
|---|---|---|
| Required credentials | Advanced degrees in engineering, mathematics, or statistics | Degree in computer science, statistics, or related fields |
| Work environment | Research labs, engineering firms, simulation-based industries | Tech companies, finance, healthcare, and marketing |
| Industry usage | Engineering, aerospace, manufacturing, scientific research | Business analytics, product development, predictive modeling |
Uncertainty Quantification focuses on assessing and reducing uncertainty in models and simulations, often requiring advanced mathematical skills. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles involve statistics and data analysis, Uncertainty Quantification is more specialized in modeling uncertainties in engineering and scientific contexts, whereas Data Scientists work across diverse industries with a broader focus on data-driven insights.
More about Uncertainty Quantification jobs
What cities are hiring for Uncertainty Quantification jobs? Cities with the most Uncertainty Quantification job openings:
What states have the most Uncertainty Quantification jobs? States with the most job openings for Uncertainty Quantification jobs include:
What job categories do people searching Uncertainty Quantification jobs look for? The top searched job categories for Uncertainty Quantification jobs are:
- Before And After School Pharma R&D Scientist
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- Operations Research Scientist Amazon
- Quantum Spatial
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- Flexible Data Science
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Senior Engineer, Aeronautical Modeling & Simulation (Dallas/San Diego) (R4538)
Shield AISan Diego, CA
Full-time
Posted 7 days ago
Job description
JOB DESCRIPTION:
The Software Integration & Operations (SIO) group turns frontier autonomy into mission-ready aircraft. We own the commit-to-flight pipeline—deterministic aircraft and mission simulation, HIL/VIL integration, CI/CD, automated flight qualification testing, and release engineering. Our goal is simple: make AI fly—safely, repeatably, and fast.
As a Modeling & Simulation Engineer, you will be responsible for improving and adding to our world and aeronautical models so that our operator training and internal engineering pipelines have a seamless translation from sim to real results.
- Develop and enhance aeronautical physics models (aerodynamics, propulsion, environmental, structural, etc.) for use in simulation and evaluation.
- Translate theoretical models into efficient, reliable C++ implementations with a focus on numerical accuracy and performance.
- Validate models against real-world data and authoritative references, including field test data and calibration procedures.
- Collaborate with simulation and training application teams to ensure models integrate cleanly into operator-facing tools.
- Design automated validation and regression testing strategies for mathematical models to ensure fidelity across releases.
- Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning–based surrogates) to push the state of the art.
- Document assumptions, equations, and validation results so that both engineers and operators can trust model outputs.
- BS or higher in Aerospace Engineering, Applied Math, Physics, or related field with 1+ years of aerospace modeling experience.
- C++ foundation with experience implementing numerical methods.
- Demonstrated experience with aerospace models such as: Aerodynamics (lift/drag curves, stability derivatives, dynamic response), Atmosphere/environment (COESA, ICAO, gravity, wind models), Vehicle dynamics.
- 1+ years of experience working on pilot/operator training systems.
- Experience with Eigen or SciPy for model prototyping and validation.
- Familiarity with state estimation sensor models (GPS, IMU, Gyro, etc) for simulation environments.
- Background in multi-body dynamics or flight mechanics
- Knowledge of uncertainty quantification and statistical analysis methods.
- Strong problem-solving mindset with a collaborative and detail-oriented approach.
- Familiarity with Python for test automation and data analysis pipelines.
- Passion for aerospace and autonomous vehicle systems.
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About Shield AI
Sourced by ZipRecruiter
Industry
Software development
Company size
11 - 50 Employees
Headquarters location
San Diego, CA, US
Year founded
2015