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Probabilistic Modeling Jobs (NOW HIRING)

xAI

Member of Technical Staff

Palo Alto, CA · On-site

Applied advanced probabilistic modeling and mathematical analysis for product optimization and safety evaluation * Experience with safety-related content classification Salary: $180,000 - $220,00 per ...

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Probabilistic Modeling information

What is the difference between Probabilistic Modeling vs Data Scientist?

AspectProbabilistic ModelingData Scientist
Required CredentialsDegree in statistics, mathematics, or related fields; knowledge of probability theoryDegree in computer science, statistics, or related fields; programming skills
Work EnvironmentResearch-focused, often in analytics or data science teamsCross-functional teams, including business, engineering, and analytics
Industry UsageUsed in analytics, finance, healthcare, and research for modeling uncertaintyApplied across industries for data analysis, predictive modeling, and decision-making

Probabilistic Modeling focuses on developing models based on probability theory to understand uncertainty, while Data Scientists utilize a broader set of skills including programming, data analysis, and machine learning to extract insights from data. Both roles often overlap but serve different primary purposes within data-driven organizations.

What is probabilistic modeling?

Probabilistic modeling is a mathematical framework used to represent uncertain events or data by using probability distributions. Instead of giving a single outcome, it accounts for variability and randomness, allowing predictions and inferences even when information is incomplete or ambiguous. Probabilistic models are widely used in fields like statistics, machine learning, finance, and engineering to analyze data, make forecasts, and support decision-making under uncertainty.

Which 3 jobs will survive AI?

Probabilistic modeling is a specialized field within data science and machine learning. Jobs that require advanced analytical skills, such as data scientists, machine learning engineers, and quantitative analysts, are likely to persist as they involve complex problem-solving and domain expertise that AI tools complement rather than replace. Continuous learning and proficiency with statistical tools and programming languages like Python or R are essential for these roles.

What is probabilistic modelling?

Probabilistic modeling is a technique used in probabilistic modeling roles to represent uncertainty and variability in data through mathematical models that incorporate probability distributions. It involves designing models that can predict outcomes and infer hidden variables, often using tools like Bayesian inference and statistical analysis. These skills are essential for data scientists and statisticians working with complex, uncertain data environments.

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

To thrive as a Probabilistic Modeler, you need a strong background in mathematics, statistics, and probability theory, often supported by a degree in applied mathematics, statistics, or a related field. Proficiency with programming languages like Python or R, and experience with statistical modeling tools and software such as TensorFlow or PyMC, are typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help translate complex models into actionable insights. These skills are vital for designing accurate models, interpreting uncertainty, and supporting data-driven decisions across various industries.

What professions make 500,000 a year?

In probabilistic modeling, senior roles such as quantitative researchers, data science directors, and machine learning engineers at large tech firms or financial institutions can earn $500,000 or more annually. These positions typically require advanced degrees, extensive experience, and expertise in statistical methods, programming, and data analysis tools. Compensation often includes base salary, bonuses, and stock options, especially in high-growth or competitive industries.

What professions make 200,000 a year without a degree?

Professions related to probabilistic modeling, such as data scientists, machine learning engineers, and quantitative analysts, can reach or exceed $200,000 annually often through experience, specialized skills, and industry demand. These roles typically require strong programming, statistical, and analytical skills, and some may be self-taught or gained through certifications rather than formal degrees.

What are some common challenges faced by professionals in probabilistic modeling roles, and how can they be managed?

Professionals in probabilistic modeling often encounter challenges such as working with incomplete or noisy data, choosing the right model complexity, and ensuring model interpretability for stakeholders. Managing these challenges involves strong statistical knowledge, regular collaboration with domain experts, and effective communication to translate complex results for non-technical team members. Staying up-to-date with the latest tools and methodologies, and participating in peer reviews, can also help maintain model accuracy and reliability.
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Probabilistic Modeling jobs near you
Probabilistic Risk Assessment (PRA) Engineer (Entry Level)

Probabilistic Risk Assessment (PRA) Engineer (Entry Level)

System One

Bellevue, WA • Remote

$37 - $74/hr

Contractor

Posted 15 days ago

Job description

Probabilistic Risk Assessment (PRA) Engineer – Entry Level (Contract)

Location: Remote Pay Rate: $37-$74/hour Position Type: Long-Term Contract

Position Overview

System One is seeking an Entry Level Probabilistic Risk Assessment (PRA) Engineer to support the development of next-generation advanced nuclear reactor technology. This role offers an excellent opportunity to work with a multidisciplinary engineering team performing probabilistic risk assessments, severe accident analysis, and safety evaluations that support reactor design, licensing, and regulatory compliance.

The selected candidate will contribute to the development and maintenance of PRA models, perform risk analyses, support hazard evaluations, and collaborate with engineering teams to ensure safe, reliable, and innovative reactor designs.

Responsibilities

  • Support internal and external Probabilistic Risk Assessment (PRA) activities.
  • Develop and maintain event trees, fault trees, and PRA models using CAFTA.
  • Perform risk integration studies, importance measures, and risk metrics.
  • Support severe accident and consequence analyses.
  • Participate in hazard analyses, PIRT evaluations, SSC classifications, Licensing Basis Events (LBE), and Defense-in-Depth (DID) evaluations.
  • Collaborate with multidisciplinary engineering teams to establish system reliability, safety, and performance requirements.
  • Prepare engineering calculations, technical documentation, and analytical reports.
  • Participate in design reviews and engineering evaluations.
  • Present technical findings to project teams, management, and regulatory organizations.
  • Support continuous improvement of PRA methodologies, computational models, and engineering processes.

Required Qualifications

  • Bachelor's degree in Nuclear, Mechanical, Electrical, Aerospace Engineering, or related discipline.
  • One or more years of Probabilistic Risk Assessment (PRA) experience.
  • Understanding of PRA principles and familiarity with ASME/ANS PRA standards.
  • Knowledge of risk-informed, performance-based regulatory approaches.
  • Experience with PRA software such as CAFTA, FRANX, SAPHIRE, RiskSpectrum, or similar tools.
  • Strong technical writing, analytical, and communication skills.
  • Ability to work effectively on multidisciplinary engineering teams.

Preferred Qualifications

  • Experience with GOTHIC, MELCOR, RADTRAD, or similar analytical software.
  • Experience with sodium-cooled or advanced reactor technologies.
  • Knowledge of severe accident analysis, human reliability analysis, source term evaluations, and accident progression.
  • Familiarity with nuclear licensing and regulatory requirements.

This is an outstanding opportunity to contribute to the development of advanced nuclear technologies while working remotely on innovative, first-of-a-kind engineering projects.

Ref: #161-Managed Staffing Charlotte

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