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Bayesian Modeling Jobs in Seattle, WA (NOW HIRING)

Opendoor

Applied Scientist- Pricing

Seattle, WA · On-site

... Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization • Comfort working with messy, high-dimensional real-world data and translating ambiguous ...

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 ...

Meta

Competitive Intelligence Research

Seattle, WA

$177K/yr

... e.g., bayesian pooling, hierarchical modeling) * Demonstrated communication skills and experience presenting complex findings to both technical and non-technical stakeholders * Demonstrated ...

Meta

Competitive Intelligence Research

Bellevue, WA

$177K/yr

... e.g., bayesian pooling, hierarchical modeling) * Demonstrated communication skills and experience presenting complex findings to both technical and non-technical stakeholders * Demonstrated ...

Exponent

Statistical Scientist (Ph.D.)

Bellevue, WA · On-site

... modeling • Applying risk analysis methodologies to problems in engineering, health, finance ... Bayesian statistics, machine learning, or quality control/improvement • Minimum of 2 years ...

Think Tank, Inc.

Statistician

Seattle, WA

Develop novel probabilistic mathematical and simulation models representing complex ecological and ... Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred.

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How much do bayesian modeling jobs pay per hour?

As of Jul 10, 2026, the average hourly pay for bayesian modeling in Seattle, WA is $66.82, according to ZipRecruiter salary data. Most workers in this role earn between $59.90 and $77.69 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.
What are popular job titles related to Bayesian Modeling jobs in Seattle, WA? For Bayesian Modeling jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Bayesian Modeling jobs in Seattle, WA look for? The top searched job categories for Bayesian Modeling jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Bayesian Modeling jobs? Cities near Seattle, WA with the most Bayesian Modeling job openings:
Bayesian Modeling jobs near you
Quantitative Analyst - COMPASS Modeling

Quantitative Analyst - COMPASS Modeling

Think Tank, Inc.

Seattle, WA

Other

Posted yesterday

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Job description

*Position is Subject to Contract Award

POSITION DESCRIPTION:

Description of Duties:

  • Develop and advance the Comprehensive Passage (COMPASS) model for application in river systems to evaluate effects of stressors and management actions across fish life cycles, using statistical modeling, computer modeling, statistical/quantitative analysis, and other quantitative modeling techniques.
  • Construct and implement models evaluating survival and migration through the hydropower system (COMPASS model) to address West Coast Regional Office requests in support of ESA litigation and permitting.
  • Collaborate with regional managers and researchers to structure, synthesize, and manage environmental and biological data relevant to modeling needs.
  • Document and share reproducible research workflows and analytical products.
  • Write reports, contribute to journal manuscripts, and present results at regional and national meetings and conferences, as needed.
  • Assist with field collection of fish and environmental data as needed (may involve riding in vehicles and/or boats).

EDUCATION & EXPERIENCE:

Required:

  • Education: Bachelor's degree or higher from an accredited college/university with a major related to the task order and a strong quantitative background, with emphasis in statistics, mathematics, fisheries, ecology, or the natural sciences.
  • Experience: Three (3) or more years of experience related to the task order.

Desired:

  • Master's degree preferred.
  • Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience

CERTIFICATIONS: 

Required:

  • Valid U.S. driver's license - required and maintained throughout the period of performance.
  • Public trust suitability; background investigation cleared prior to beginning performance.
  • Government-required training to be completed within 5 business days of start: NOAA IT Security, NOAA Safety, Sexual Assault/Sexual Harassment Prevention & Response (NAM 1330-52.222-70(b)(6)), and Records Management 101.

RESPONSIBILITIES:

Required (Deliverables):

  • All analyses conducted with accepted methods and QA/QC.
  • Assigned statistical and biological modeling components completed and integrated into COMPASS and life cycle models for the Columbia River basin, and others as needed.
  • Reproducible analytical workflows documented; modeling scripts, methods, and results shared via open-science platforms (e.g., GitHub).
  • Written status reports and other ad hoc communications; participation in field tasks as needed.

SKILLS:

Required:

  • Extensive experience in modeling and data analysis; extensive experience executing statistical analysis and modeling in R, with strong R coding skills.
  • Familiarity interpreting or interfacing with C or C++ code within a scientific modeling context.
  • Familiarity with common workplace software such as Google Suite and Microsoft Office.
  • Excellent communication skills; experience writing reports and contributing to peer-reviewed articles; able to work independently and on interdisciplinary teams.

  • Desired:

    • Experience with Bayesian statistical methods and mark-recapture analysis preferred.
    • Computational experience with command-line environments (e.g., Linux/shell scripting) preferred; experience with open-science concepts.
    • Experience with Bayesian modeling programs Stan and JAGS preferred.

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