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Bayesian Modeling Jobs in Seattle, WA (NOW HIRING)

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

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 ...

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

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

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

... Bayesian optimization and other multi-objective optimization techniques) to optimize LLM models at datacenter scale. * Research, develop, and deploy AI/ML techniques to optimize large-scale Deep ...

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Bayesian Modeling information

See Seattle, WA salary details

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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 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 cities near Seattle, WA are hiring for Bayesian Modeling jobs? Cities near Seattle, WA with the most Bayesian Modeling job openings:
Infographic showing various Bayesian Modeling job openings in Seattle, WA as of June 2026, with employment types broken down into 4% As Needed, 53% Full Time, 23% Part Time, 4% Temporary, 12% Contract, and 4% Nights. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $138,979 per year, or $66.8 per hour.
Scientist I - ML/AI algorithms for Multimodal Foundational Models for Gene Regulation

Scientist I - ML/AI algorithms for Multimodal Foundational Models for Gene Regulation

Allen Institute

Seattle, WA • Hybrid

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 days ago


Job description

Scientist I – ML/AI algorithms for Multimodal Foundational Models for Gene Regulation

The mission of the Allen Institute is to understand the principles that govern life, and to advance health. Our creative and multi-dimensional teams focus on answering some of the biggest questions in bioscience. We accelerate foundational research, catalyze bold ideas, develop tools and models, and openly share our science to make a broad, transformational impact on the world. The mission of the Allen Institute for Brain Science is to accelerate the understanding of how the human brain works in health and disease. Using a big science approach, we generate useful public resources, drive technological and analytical advances, and discover fundamental brain properties through the integration of experiments, modeling, and theory.

The Seattle Alzheimer’s Disease Brain Cell Atlas (SEA-AD) consortium strives to achieve a deep molecular and cellular understanding of the early pathogenesis of Alzheimer’s disease (AD). Leveraging advances in quantitative neuropathology and next-generation single cell molecular profiling technologies we are generating large-scale data sets from well-characterized donors that span the spectrum of AD pathology to comprehensively map human brain cell types across aging and disease. These data along with tools for their use are made freely and publicly available.

We seek to hire a Research Scientist to design modern machine learning methods to integrate multimodal data and describe disease trajectories and contribute to the mechanistic understanding of Alzheimer’s disease pathology. The successful candidate will have a strong background in computational biology, and experience developing deep generative models and Bayesian algorithms. In addition, the ideal candidate will either have experience in causal inference or gene regulatory network inference, or has worked on aspects of gene regulation in disease.

Strong preference will be given to individuals with a track record of both individual and team contributions in solving complex research problems, and experience in cutting-edge computational methodologies applied to biological -omics, spatial, pathological, and/or clinical metadata.

At the Allen Institute, we believe that science is for everyone – and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly.

We also believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.

We are an equal-opportunity employer and strongly encourage people from all backgrounds to apply for our open positions.

Essential Functions

  • Develop modern machine learning algorithms to model disease progression from multimodal data (omics, neuropathology, MRI, genetic information, clinical histories)
  • Develop Bayesian statistical models of neurodegenerative progression
  • Evaluate models that can harmonize multiple cohort information
  • Develop causal models of disease progression
  • Stay at the forefront of advances in AI for multimodal disease progression modeling
  • Participate in a highly interactive and multidisciplinary environment
  • Publish/present findings in peer-reviewed journals/scientific conferences

Note: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

Required Education and Experience

  • Ph.D. in Computer Science, Applied Math, Engineering, Computational Neuroscience, Computational Biology, or related field, or equivalent combination of degree and experience
  • Experience working with recent Deep Learning Architectures/Foundational Models
  • Experience with Bayesian modeling and inference
  • Experience developing causal models

Preferred Education and Experience

  • Experience with current ML models such as score-based diffusion models, multimodal data fusion transformer architectures, or state-space models.
  • Proficiency with cloud computing and with on-prem clusters
  • Strong publication track record
  • Proven ability to work independently and manage multiple projects simultaneously while meeting deadlines in a highly collaborative environment
  • Excellent written and verbal communication skills, with the ability to collaborate effectively in a multidisciplinary team environment.

Physical Demands

  • Occasional lifting up to 30 pounds (reference: a ream of paper weighs approx. 5lbs
  • Fine motor movements in fingers/hands to operate computers and other office equipment; repetitive motion with lab equipment.

Position Type/Expected Hours of Work

  • This role is currently able to work both remotely and onsite in a hybrid work environment. We are a Washington State employer, and the primary work location for all Allen Institute employees is 615 Westlake Ave N.; any remote work must be performed in Washington State.

Travel

  • Occasional attendance and participation in national and international conferences

Additional Comments

  • **Please note, this opportunity offers relocation assistance**
  • **Please note, this opportunity may offer visa sponsorship**

Annualized Salary Range

$86,500 - $106,500 *

* Final salary depends on the required education for the role, experience, level of skills relevant to the role, and work location, where applicable.

Benefits

Employees (and their families) are eligible to enroll in benefits per eligibility rules outlined in the Allen Institute’s Benefits Guide. These benefits include medical, dental, vision, and basic life insurance. Employees are also eligible to enroll in the Allen Institute’s 401k plan. Paid time off is also available as outlined in the Allen Institutes Benefits Guide. Details on the Allen Institute’s benefits offering are located at the following link to the Benefits Guide: https://alleninstitute.org/careers/benefits.

It is the policy of the Allen Institute to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Allen Institute will provide reasonable accommodations for qualified individuals with disabilities.