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Bayesian Networks Jobs (NOW HIRING)

SRP Systems Inc

Senior Data Scientist

Princeton, NJ · On-site

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 ...

SRP Systems Inc

Senior Data Scientist

Princeton, NJ · On-site

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 ...

Pop-Up Talent

Senior Research Engineer

Santa Clara, CA · On-site

$200K/yr

... world cellular networks * This role combines estimation theory, RF propagation, statistical ... Formulate and solve complex inference problems using Bayesian estimation, filtering, optimization ...

CB Smart Recruit

Senior AI/ML Engineer

Los Angeles, CA

$180K - $350K/yr

... networks, structured enterprise data, and open-source intelligence (OSINT). These diverse data ... Bayesian Inference & Probabilistic Modeling * Build Bayesian inference pipelines supporting real ...

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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.
Data Scientist

Data Scientist

Avani Technology Solutions, Inc.

San Francisco, CA • On-site

Full-time

Re-posted 24 days ago

Job description

Role: Data Scientist
Location: San Francisco, CA
Duration: 6+ Months
Team Overview:
The Digital Catalyst Team is a new enterprise team that is responsible for working collaboratively with the lines of business to implement consumer grade mobile and analytical solutions across various user groups (e.g., field users, office workers, etc.). This includes, but is not limited to:
  • Deploying best-in-class / rapid delivery capability for mobile solutions.
  • Simplifying, improving, and standardizing business work management processes for mobile needs.
  • Delivering high value analytics across all Lines of Businesses.
  • Rapid delivery of web applications.

Digital Catalyst consists of a staff of highly skilled professionals working together to produce mobile solutions following an agile methodology and design thinking. We are a "start-up" department within IT and building driven and creative mobile development team. We take the time to understand our partners' needs and translate those into solutions that delight our users. Our goal is to deliver products with intuitive user experience that will improve employees' and customer's safety, productivity and overall well-being.
Position Summary:
We are seeking an experienced Data Scientist in the Digital Catalyst Team who will provide strong execution and delivery of data science. Working as a part of the product team, this Data Scientist will translate business needs into advanced analytics and machine learning models. The successful candidate will be responsible for model selection and identification of appropriate training data sets; building, training, and evaluating models; and delivering results to the business on a regular cadence. This role is part of a fully Agile Scrum team, so the data scientist will work alongside a product owner, technical lead, and team of developers and data engineers to support delivery of high-value analytics and software products.
Position Responsibilities:
  • Leads development of high complexity models and training sets
  • Provides hands-on execution and implementation of data science models
  • Translates business analysis needs into well-defined data science problems, and selecting appropriate models and algorithms and communicates model evaluation and implications of results back to stakeholders
  • Recognizes and prioritizes the most important work related to data science models to achieve highest operational impact for analytics in the business
  • Balances tradeoffs among analytics value, model development methods and design and technologies used to implement data science models with a bias toward action
  • Performs collaborative work on data science problems and mentor junior data scientists
  • Creates shared process models, business objects, activity diagrams and process documentation to effectively articulate multiple views of the business solutions that support technical architecture.
  • Manages development of quantitative models and tools.
  • Collaborates with leaders, other LOBs, and business partners to work on issues, projects or activities.
  • Develops new or revises complex models to predict business demand trends, and volume and expenditures forecasts capacity analysis, and various other metrics to identify potential opportunities.
  • Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
  • Partners with leaders to drive high performance in their lines of business.
  • Develop deep understanding of business drivers and financial levers to provide strategic decision support.
  • Oversees resolution of complex projects and programs.
  • Develops and maintains up-to-date detailed project schedules and work plans.
  • Performs analysis on complex data models requiring customized reports and data and presents recommendations.

Minimum Education/Skills:
  • Bachelor's Degree in Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics or job-related discipline or equivalent experience
  • Job-related experience, 8 years, OR Master's Degree and job-related experience, 6 years, OR Doctorate Degree and job-related experience, 3 years
  • Experience in data modeling, 5yrs

Desired Education / Skills:
  • PhD in engineering or a related field (computer science, natural sciences, mathematics)
  • Experience with Python, R, Scala, SQL
  • Experience developing solutions with Pandas/Scikit-learn, Spark or comparable technologies
  • Experience data science notebooks (Jupyter, Zeppelin or other)
  • Experience with AWS, Azure, cloud computing technologies
  • Scrum team experience
  • Energy industry experience
  • Experience designing efficient data science workflows and database architecture for data science purposes
  • Experience with forecasting, Bayesian networks, and graph analytics
  • Strong statistics experience
  • Experience with software development methodologies and software engineering principles
  • Knowledge of program management theories, concepts, methods, best practices, and techniques as needed to perform at the job level
  • Knowledge of relevant programming languages - for example Visual Basic, Ladder Logic,
  • Programmable Logic Controller, C, SharePoint, HTML, Java, Adobe - as needed to perform at the job level
  • Competency in knowing the most effective and efficient processes to get things done, with a focus on continuous improvement
  • Knowledge of principles, techniques, and procedures used for production and design of technology based equipment and systems as needed to perform at the job level
  • Knowledge of statistical theories, concepts, methods, best practices, and analyses as needed to perform at the job level
  • Ability to develop reports, models, and simulations as needed to perform at the job level
  • Competency in developing and delivering multi-mode communications that convey a clear
  • understanding of the unique needs of different audiences
  • Knowledge of data model design philosophies and methodologies for data warehouse and OLTP systems

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