1

Bayesian Networks Jobs (NOW HIRING)

Highly conversant with Bayesian type algorithms, KNN, Neural Networks, SVM, etc * Experienced in applying data science techniques on multiple industries and exposure to their sets is necessary * Data ...

Develop and deploy machine learning models, including deep neural networks (DNNs), convolutional neural networks (CNNs), and Bayesian neural networks (BNNs), to solve nuclear engineering problems.

Develop and deploy machine learning models, including deep neural networks (DNNs), convolutional neural networks (CNNs), and Bayesian neural networks (BNNs), to solve nuclear engineering problems.

Develop and deploy machine learning models, including deep neural networks (DNNs), convolutional neural networks (CNNs), and Bayesian neural networks (BNNs), to solve nuclear engineering problems.

Showing results 41-60

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.
More about Bayesian Networks jobs
What cities are hiring for Bayesian Networks jobs? Cities with the most Bayesian Networks job openings:
What states have the most Bayesian Networks jobs? States with the most job openings for Bayesian Networks jobs include:
Infographic showing various Bayesian Networks job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 67% In-person, and 33% Remote job distribution.

Senior Data Scientist

SRP Systems Inc

Princeton, NJ • On-site

Full-time

Re-posted 20 days ago


Job description

Company Description
SRP is a big data company located in Princeton, NJ focused on Dynamic Pricing, run by seasoned alumni from Stanford University and Wharton.
Job Description
Title: Senior Data Scientist for a Stanford Startup
Location: Princeton, NJ
Full Time
Job Requirements:
  • A minimum of three years of experience working as a data scientist
  • Experience in working with large data sets
  • Strong experience in machine learning
  • Highly conversant with Bayesian type algorithms, KNN, Neural Networks, SVM, etc
  • Experienced in applying data science techniques on multiple industries and exposure to their sets is necessary
  • Data cleanup, and data massaging experience
  • Hands-on experience with R or Python type languages
  • Any experience in doing well at Kaggle type competitions is a big plus

Qualifications
Additional Information
All your information will be kept confidential according to EEO guidelines.