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Bayesian Networks Jobs (NOW HIRING)

UMass Med School

Computational Biologist

Worcester, MA · On-site

$90K/yr

Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian networks). * Combine data from ...

Sunrise Systems, Inc.

Senior Data Scientist

San Ramon, CA · On-site

$100 - $105/hr

Experience with forecasting, Bayesian networks, and graph analytics * Utility/Energy industry experience * Foundry experience preferred * Experience working in Agile/Scrum environments * Strong ...

CYNET SYSTEMS

Data Scientist

San Ramon, CA · On-site

$93 - $98/hr

Experience with forecasting, Bayesian networks, and graph analytics. * Knowledge of program management theories, concepts, methods, best practices, and techniques. Skills: * Strong statistics ...

Huntington

Senior Data Scientist - AI

Columbus, OH · On-site +1

Strong understanding of statistical methods and skills such as Bayesian Networks Inference, linear and non-linear regression, hierarchical, mixed models/multi-level modeling * Financial Services ...

Huntington

Senior Data Scientist - AI

Columbus, OH · On-site +1

... as Bayesian Networks Inference, linear and non-linear regression, hierarchical, mixed models/multi-level modeling Financial Services background Exempt Status: (Yes = not eligible for overtime pay ...

Huntington

Senior Data Scientist - AI

Columbus, OH · On-site +1

Strong understanding of statistical methods and skills such as Bayesian Networks Inference, linear and non-linear regression, hierarchical, mixed models/multi-level modeling * Financial Services ...

Utah State University

Postdoctoral Fellow I

Logan, UT · On-site

$42K - $57K/yr

Knowledge about PINs, graphical models such as the dynamic Bayesian networks. Required Documents Along with the online application, please attach: 1. Resume/CV to be uploaded at the beginning of your ...

Utah State University

Postdoctoral Fellow I

Logan, UT · On-site

$42K - $57K/yr

Knowledge about PINs, graphical models such as the dynamic Bayesian networks. Along with the online application, please attach: 1. Resume/CV to be uploaded at the beginning of your application in the ...

Sidram Technologies

Data Scientist - w2

Columbus, OH · On-site

Understanding of statistical methods and skills such as Bayesian Networks Inference, linear and non-linear regression, hierarchical, mixed models/multi-level modeling * Experience with Cloud Machine ...

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Bayesian Networks information

What is the difference between Bayesian Networks vs Data Analysts?

AspectBayesian NetworksData Analysts
Required CredentialsStatistics, Data Science, Computer Science degrees; certifications in probabilistic modelingStatistics, Data Science, Business Analytics degrees; certifications in data analysis tools
Work EnvironmentResearch, modeling, and algorithm development in tech or research firmsData interpretation, reporting, and visualization across various industries
Industry UsageUsed for probabilistic reasoning, decision support, and machine learningUsed for data interpretation, reporting, and business insights

Bayesian Networks focus on probabilistic modeling and decision-making algorithms, often requiring advanced statistical knowledge. Data Analysts primarily interpret and visualize data to inform business decisions. While both roles involve data, Bayesian Networks are more technical and model-driven, whereas Data Analysts focus on data interpretation and reporting.

What are Bayesian Networks?

Bayesian Networks are probabilistic graphical models that represent a set of variables and their conditional dependencies using a directed acyclic graph. They are used to model uncertainty in complex systems by encoding relationships between variables and allowing for efficient inference and reasoning. These networks are widely applied in fields such as machine learning, diagnostics, decision support, and bioinformatics to help predict outcomes and understand causal relationships.

What are the key skills and qualifications needed to thrive as a Bayesian Networks Specialist, and why are they important?

To thrive as a Bayesian Networks Specialist, you need a strong background in statistics, probability theory, and machine learning, often supported by a degree in computer science, mathematics, or a related field. Proficiency with programming languages such as Python or R, and experience using specialized libraries like pgmpy or bnlearn, are typically required. Strong analytical thinking, problem-solving ability, and effective communication skills set standout professionals apart in this role. These competencies are crucial for designing, implementing, and interpreting Bayesian models that inform critical decision-making in complex domains.

What are some common challenges faced by professionals working with Bayesian Networks in real-world projects?

Professionals working with Bayesian Networks often encounter challenges such as handling incomplete or noisy data, defining accurate conditional dependencies, and ensuring computational efficiency for large or complex networks. Collaboration with domain experts is crucial to correctly structure the network and validate assumptions. Additionally, integrating Bayesian models with existing data systems and effectively communicating probabilistic results to non-technical stakeholders are important aspects of the role.
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Bayesian Networks jobs near you
Infographic showing various Bayesian Networks job openings in the United States as of July 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 67% In-person, and 33% Remote job distribution.

Computational Biologist

UMass Med School

Worcester, MA • On-site

$90K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Computational Biologist
Minimum Salary US-MA-Worcester
Job Location 1 week ago(7/10/2026 9:17 AM)
Requisition Number 2026-49909 # of Openings 1 Posted Date Day Shift Exempt Position Type Full-Time Min USD $80,000.00/Yr. Max USD $90,000.00/Yr.
Overview

The Computational Biologist be part of an interdisciplinary research group combining systems biology, immunology, and human genetics to uncover the mechanisms that drive autoimmune disease. The lab leads large-scale efforts such as the VIGOR family-based vitiligo cohort (bigor.umassmed.edu) and multi-omic studies of lupus and cutaneous autoimmunity, integrating data across molecular, cellular, and clinical scales.

This position will bridge two complementary areas of research:

    Molecular systems immunology, involving the analysis of single-cell and spatial transcriptomic, epigenomic, and proteomic datasets to dissect cell states and communication networks in diseased and healthy tissues.
  1. Genetic and longitudinal modeling, integrating genomic variation with real-world longitudinal data-including proteomics, wearable device metrics, survey responses, and clinical measures-to build predictive and causal models of disease initiation and progression.

The ideal candidate combines strong computational and statistical skills with a biological curiosity about how genetic and environmental factors jointly shape immune dysregulation.

Responsibilities

Responsibilities

  • Process, analyze, and interpret large-scale datasets including bulk and single-cell RNA-seq, ATAC-seq, proteomics, and spatial transcriptomics.
  • Develop new analysis methods as needed and as they arise during investigations
  • Perform clustering, trajectory inference, and regulatory network reconstruction to define immune cell states and pathways relevant to autoimmune pathogenesis.
  • Work closely with clinicians, immunologists, and experimentalists to formulate biologically grounded hypotheses and computational analyses.
  • Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian networks).
  • Combine data from wearable sensors (e.g., Fitbit activity, sleep, heart rate), clinical surveys, and biomarker measurements to model temporal dynamics of disease activity.
  • Present findings in lab meetings, consortium calls, and scientific conferences; contribute to manuscripts and grant proposals.
  • Generate publication-quality figures and interactive visualizations that communicate complex data intuitively.
Qualifications

Required Qualifications

  • Master's degree in Computational Biology, Bioinformatics, Genetics, Statistics, Physics, Math or a related quantitative field; Ph.D. strongly preferred.
  • 1-3 years of related experience
  • Strong proficiency in R or Python, statistical modeling, and data visualization.
  • Strong understanding of linear models, mixed-effect models, and in general machine learning approaches to complex datasets.
  • Experience working in Unix/Linux environments and using HPC or cloud-based computational resources.

Preferred Qualifications

  • Background in human genetics or clinical genomics, including genotype imputation, association testing, and fine-mapping.
  • Experience with integrative or multi-omic data analysis and familiarity with single-cell and spatial transcriptomic data.
  • Knowledge of causal inference, longitudinal modeling, or Bayesian hierarchical modeling.
  • Exposure to wearable-device or digital-phenotyping datasets and experience linking such data to molecular or clinical outcomes.
  • Understanding of immunology or autoimmune disease biology.
  • Familiarity with containerization (Docker/Singularity), workflow management systems (Snakemake, Nextflow), and reproducible-research practices.
Additional Information

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