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Bayesian Modeling Jobs (NOW HIRING)

InMobi

Applied Scientist III

San Mateo, CA

Design and experiment with methods in online learning, reinforcement learning, multi-armed bandits, forecasting, game theory, and Bayesian modeling-in non-stationary, adversarial environments.

Roberts Recruiting

Senior Data Scientist

Boston, MA

Understand Bayesian modeling techniques. * Are capable of analyzing data and making rigorous statements about what can or cannot be concluded. * Have experience designing and implementing model ...

Walmart

(USA) Senior Manager, Data Science

Elkins, 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

Goshen, 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.

AEG

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 ...

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 ...

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How much do bayesian modeling jobs pay per hour?

As of May 30, 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 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 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.

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Bayesian Modeling jobs near you
Post Doc - Open Rank

Post Doc - Open Rank

University of Massachusetts Medical School

Worcester, MA • On-site

$48.80K - $66.30K/yr

Other

Posted 26 days ago

Job description

Overview

Postdoctoral Position in Population Genetics and Machine Learning of Autoimmunity

The Garber Lab at the University of Massachusetts Chan Medical School (UMass Chan) invites applications for a Postdoctoral Research Associate to join our multidisciplinary team studying the genetic and molecular mechanisms driving autoimmune and inflammatory skin diseases. Our group integrates population genetics, statistical modeling, and single-cell and spatial multi-omics to understand how genetic variation and immune pathways converge to cause disease. We are a core component of the VIGOR study (vigor.umassmed.edu), a large-scale longitudinal study of vitiligo and related autoimmune conditions, and collaborate extensively with clinical and computational teams to translate genomic insights into personalized medicine approaches.

Responsibilities

The successful candidate will lead analyses spanning genomic and clinical data integration, including:

  • Performing QTL mapping (eQTL, sQTL, and caQTL) across single-cell and bulk data modalities 
  • Developing and applying polygenic risk scores and causal inference models to predict disease onset, progression, and treatment response 
  • Implementing machine learning and statistical genetics frameworks to integrate longitudinal clinical, environmental, and wearable-derived data 
  • Designing computational approaches for spatial transcriptomics and spatial genomics data to identify key cellular and molecular drivers of local inflammation 
  • Contributing to the development of computational methods for integrating genetics with spatial and temporal immune responses
  • The position provides opportunities to develop and publish innovative computational methods and to contribute to high-impact translational studies of autoimmunity.

Our overarching goal is to define the genetic underpinnings of autoimmune skin diseases by understanding how genetic variability alters immune cell responses that tilt the balance toward autoimmunity. Building on our recent studies that revealed disease-associated dendritic cell states and cytokine-driven spatial programs of inflammation, the postdoctoral researcher will have access to a rich resource of single-cell, spatial, and longitudinal clinical datasets generated by our NIH-funded consortium.

Qualifications
  •  Ph.D. (or equivalent) in Genetics, Computational Biology, Bioinformatics, Biostatistics, Computer Science, or a related field
  • Demonstrated expertise in population genetics, statistical modeling, or machine learning - Experience with large-scale genomic data analysis (e.g., GWAS, QTL, PRS, or multi-omics integration)
  • Strong programming skills in R or Python; familiarity with Bayesian modeling, causal inference, or deep learning is a plus
  • Excellent communication skills and enthusiasm for collaborative, interdisciplinary research
Additional Information

The Garber Lab is part of a vibrant computational and systems biology community at UMass Chan, providing access to state-of-the-art genomics technologies, clinical cohorts, and cross-disciplinary mentorship. Our team values rigorous quantitative science, open collaboration, and mentorship-driven career development.

Interested candidates should send a CV, a brief statement of research interests, and contact information for three references to Manuel Garber, Ph.D., Professor of Genomics and Computational Biology.

(manuel.garber@umassmed.edu)

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