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Bayesian Networks Jobs (NOW HIRING)

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

... Bayesian networks, structural causal models) • Experience building or working with knowledge graphs (Neo4j, RDF, graph databases) • Understanding of explainable AI techniques (SHAP, LIME ...

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

... g., Bayesian networks, structural causal models) Experience building or working with knowledge graphs (Neo4j, RDF, graph databases) Understanding of explainable AI techniques (SHAP, LIME ...

Chabez Tech

Machine Learning Engineer

Portland, OR

... Bayesian networks, structural causal models) • Experience building or working with knowledge graphs (Neo4j, RDF, graph databases) • Understanding of explainable AI techniques (SHAP, LIME ...

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 ...

New

Cynet Systems

Data Scientist

San Ramon, CA · On-site

$93.14 - $98.14/hr

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

New

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 ...

New

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

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 ...

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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 June 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 67% In-person, and 33% Remote job distribution.

Machine Learning Engineer

Chabez Tech

Portland, OR • On-site

Contractor

Posted 19 hours ago

Job description

Company Description
Job Description
Job Title: Machine Learning Engineer
Location: Portland, OR - Onsite (Local only / F2F interview)
Duration: 24 Months Contract
Experience Level: 5+ years of experience
Required Qualifications
• Bachelor's or master's degree in computer science, Machine Learning, Electrical Engineering, or related field
• 5+ years of experience in machine learning, data science, or AI engineering
• Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow)
• Experience with time-series data analysis and anomaly detection
• Hands-on experience with causal inference methods (e.g., Bayesian networks, structural causal models)
• Experience building or working with knowledge graphs (Neo4j, RDF, graph databases)
• Understanding of explainable AI techniques (SHAP, LIME, counterfactual analysis)
• Experience deploying ML models in production systems
• Strong problem-solving skills and ability to work with complex, real-world datasets
Preferred Qualifications
• Experience with fault tree analysis (FTA), reliability engineering, or failure analysis
• Background in industrial systems, semiconductors, manufacturing, or IoT environments
• Experience with graph-based ML / Graph Neural Networks (GNNs)
• Familiarity with RCA methodologies (FMEA, 5 Whys, fishbone diagrams)
• Experience with vector databases, RAG systems, or LLM-based reasoning
• Knowledge of MLOps practices (CI/CD, monitoring, model governance)
• Experience working in air-gapped or high-security environments
Qualifications
Additional Information
All your information will be kept confidential according to EEO guidelines.

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