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Bayesian Modeling Jobs (NOW HIRING)

Arva Intelligence

Modeling Scientist

Houston, TX · On-site

$100K - $160K/yr

Develop hierarchical and Bayesian approaches to support distributed and iterative model optimization * Apply probabilistic methods to integrate data, models, and uncertainty across scenarios

ASM Global

Manger, Modeling Insights

Frisco, TX · On-site

$90K - $100K/yr

Build and maintain statistical, Bayesian, and machine learning models for use cases including lead scoring, customer retention, demand forecasting, and segmentation. * Apply Bayesian and ...

Walmart

(USA) Senior Manager, Data Science

Noel, MO · On-site

$110K - $220K/yr

This role focuses on developing a state-of-the-art, probabilistic Media Mix Model (MMM) powered by Bayesian statistics and streamlined through innovative Agentic AI workflows. What you'll do.

Arva Intelligence

Modeling Scientist

Houston, TX · On-site +1

$100K - $160K/yr

Develop hierarchical and Bayesian approaches to support distributed and iterative model optimization * Apply probabilistic methods to integrate data, models, and uncertainty across scenarios

New

Mercor

Bayesian Statistics Expert - PhD

San Francisco, CA · Remote

$70 - $100/hr

Position: STEM Computational Scientific Software & Evaluation Design - Computational Bayesian ... Calibrate tasks against state-of-the-art AI models and refine problem designs to achieve target ...

Walmart

(USA) Senior Manager, Data Science

Johnson, AR · On-site

$110K - $220K/yr

This role focuses on developing a state-of-the-art, probabilistic Media Mix Model (MMM) powered by Bayesian statistics and streamlined through innovative Agentic AI workflows. What you'll do.

Opendoor

Applied Scientist- Pricing

Seattle, WA · On-site

$156K - $335K/yr

Experience with one or more of the following: causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization * Comfort working with messy ...

Walmart

(USA) Senior Manager, Data Science

Rogers, AR

$110K - $220K/yr

This role focuses on developing a state-of-the-art, probabilistic Media Mix Model (MMM) powered by Bayesian statistics and streamlined through innovative Agentic AI workflows. What you'll do.

Walmart

(USA) Senior Manager, Data Science

Lowell, AR

$110K - $220K/yr

This role focuses on developing a state-of-the-art, probabilistic Media Mix Model (MMM) powered by Bayesian statistics and streamlined through innovative Agentic AI workflows. What you'll do.

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Bayesian Modeling information

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How much do bayesian modeling jobs pay per hour?

As of Jun 20, 2026, the average hourly pay for bayesian modeling in the United States is $58.71, according to ZipRecruiter salary data. Most workers in this role earn between $52.64 and $68.27 per hour, depending on experience, location, and employer.

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 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.
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What cities are hiring for Bayesian Modeling jobs? Cities with the most Bayesian Modeling job openings:
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What job categories do people searching Bayesian Modeling jobs look for? The top searched job categories for Bayesian Modeling jobs are:
Bayesian Modeling jobs near you
Infographic showing various Bayesian Modeling job openings in the United States as of June 2026, with employment types broken down into 17% As Needed, 17% Full Time, 17% Temporary, 32% Contract, and 17% Nights. Highlights an 64% Physical, 3% Hybrid, and 33% Remote job distribution, with an average salary of $122,123 per year, or $58.7 per hour.

Modeling Scientist

Arva Intelligence

Houston, TX • On-site

$100K - $160K/yr

Full-time

Posted 3 days ago

Job description

Job Title: Modeling Scientist (Uncertainty Quantification)
Department: Modeling & Analytics
Reports to: Lead Modeling Scientist
Location: Remote
Base Salary Range: $100k - $160k base salary
The Modeling Scientist is responsible for improving model traceability, uncertainty quantification, and predictive trustworthiness in Arva's ecosystem model predictions. This role is central to advancing Arva's monitoring, reporting, and verification platform for greenhouse gas emission reductions and removals.
Working at the intersection of statistics, machine learning, and process-based ecosystem modeling, this role works closely with ecosystem modelers and data engineers to design robust model traceability and uncertainty frameworks that support transparent, decision-ready outputs for customers, partners, and environmental markets. The Modeling Scientist plays a critical role in translating scientific rigor into real-world impact through credible, auditable modeling systems.
Primary Job Responsibilities
Uncertainty Quantification and Model Evaluation
  • Generate and apply model traceability framework for ecosystem and biogeochemical models to enable rigorous model testing and improvements
  • Design and implement uncertainty quantification framework for the models, including parameter, structural, aleatory, and epistemic uncertainties
  • Apply sensitivity analysis, multivariate testing, and cross-validation to evaluate model robustness and generalizability across space and time
  • Quantify and communicate model confidence, uncertainty bounds, and performance metrics

Statistical and Probabilistic Modeling
  • Develop hierarchical and Bayesian approaches to support distributed and iterative model optimization
  • Apply probabilistic methods to integrate data, models, and uncertainty across scenarios
  • Analyze model outputs to diagnose limitations and inform model improvement strategies

Machine Learning and Model Integration
  • Integrate machine learning techniques with process-based or mechanistic models to improve predictive performance and scalability
  • Partner with data engineers to implement reproducible, scalable modeling pipelines
  • Contribute to the design of model evaluation and optimization workflows

Scientific Communication and Documentation
  • Communicate uncertainty, confidence intervals, and model performance clearly to internal teams and external stakeholders
  • Contribute to scientific reports, transparent model documentation, and peer-reviewed publications as appropriate
  • Support defensible, auditable model outputs suitable for regulatory and credit market review

Key Competencies / Requirements
  • 5+ years demonstrated experience in uncertainty quantification, probabilistic modeling, and data model integration
  • Advanced proficiency in Python and scientific computing, with experience building reproducible modeling pipelines
  • Strong software engineering practices, including writing modular, testable, and well-documented code
  • Deep commitment to scientific rigor, transparency, and integrity
  • Experience integrating machine learning with process-based or mechanistic models preferred
  • Familiarity with ecosystem or Earth system models such as DayCent or CESM preferred
  • Familiarity with cloud platforms and data systems, including AWS and relational or spatial databases, preferred
  • Master's or PhD degree or equivalent experience in Statistics, Applied Mathematics, Environmental Science, Earth System Science, Biology, or a related quantitative field

Responsibilities:
  • Generate and apply a model traceability framework for ecosystem and biogeochemical models to enable rigorous model testing and improvements.
  • Design and implement an uncertainty quantification framework, including parameter, structural, aleatory, and epistemic uncertainties.
  • Apply sensitivity analysis, multivariate testing, and cross-validation to evaluate model robustness and generalizability.
  • Quantify and communicate model confidence, uncertainty bounds, and performance metrics.
  • Develop hierarchical and Bayesian approaches for distributed and iterative model optimization.
  • Apply probabilistic methods to integrate data, models, and uncertainty across scenarios.
  • Analyze model outputs to diagnose limitations and inform model improvement strategies.
  • Integrate machine learning techniques with process-based models to improve predictive performance.
  • Partner with data engineers to implement reproducible, scalable modeling pipelines.
  • Contribute to the design of model evaluation and optimization workflows.
  • Communicate uncertainty, confidence intervals, and model performance clearly to stakeholders.
  • Contribute to scientific reports, model documentation, and peer-reviewed publications.
  • Support defensible, auditable model outputs for regulatory and credit market review.

Employment Eligibility
Only applicants currently, and in the future, eligible to work in the United States will be considered for this position.
Summary: The Modeling Scientist is responsible for enhancing model traceability, uncertainty quantification, and predictive trustworthiness within Arva's ecosystem model predictions. This role is pivotal in advancing Arva's platform for monitoring, reporting, and verifying greenhouse gas emission reductions and removals. Collaborating at the intersection of statistics, machine learning, and process-based ecosystem modeling, the Modeling Scientist ensures robust model traceability and uncertainty frameworks, delivering transparent, decision-ready outcomes for customers, partners, and environmental markets.

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