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Bayesian Networks Jobs in Maryland (NOW HIRING)

Barrow Wise Consulting

AI Engineer

MD · On-site

$80K - $160K/yr

... as Bayesian, coordinate descent, gradient descent, and evolutionary * Utilizes big data computation and storage models to create prototypes and data sets * Develop Convolutional Neural Networks and ...

NetSage

AI Software Engineer-Principal

Annapolis, MD · On-site

$133K - $179K/yr

Experience with modern deep learning architectures, including deep neural networks (DNNs ... Strong understanding of statistical modeling, probability theory, Bayesian inference, covariance ...

Pae

Principal AI Software Engineer TS/SCI w/Poly

Fort George G Meade, MD · On-site

$149K - $200K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... neutral networks (DNN), recurrent neural networks (RNN), attention-based transformers, etc ... Bayesian analysis, and covariance matrices. * Proficiency in agentic systems that can automatically ...

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Bayesian Networks information

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 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.

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 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.

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What job categories do people searching Bayesian Networks jobs in Maryland look for?

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What cities in Maryland are hiring for Bayesian Networks jobs?

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Bayesian Networks jobs near you

Principal AI Software Engineer (TS/SCI + Poly)

Aperio Global

Fort George G Meade, MD

$149K - $200K/yr

Full-time

Re-posted 24 days ago

Job description

We are seeking a Principal AI Software Engineer to support the exciting Corporate Directory Services mission. You will work closely with mission stakeholders to lead the development of advanced Artificial Intelligence and Machine Learning (AI/ML) solutions, intelligence mission workflows, and emerging analytic capabilities. This role is responsible for designing, developing, prototyping, and operationalizing AI-enabled applications that automate complex intelligence processes, discover emergent intelligence value, and accelerate mission outcomes. You will also  work across multiple technical domains including software engineering, data science, machine learning, and large language model (LLM) integration to deliver innovative capabilities from proof-of-concept through operational deployment.

Mission Focus:

Collaborate closely with customers to drive the rapid development, evaluation, and transition of AI/ML-enabled mission capabilities that enhance operational analysis and intelligence workflows. Leverage emerging technologies, scalable software architectures, and advanced analytics to automate discovery, accelerate insight generation, and deliver production-ready solutions across diverse mission domains. Shape technical strategy and best practices to ensure effective deployment, sustainment, and evolution of AI-enabled mission systems

Technical Proficiency:

Proficient in current AI/ML technologies such as classification, clustering, collaborative filtering, search, and retrieval using techniques such as but not limited to  deep neutral networks (DNN), recurrent neural networks (RNN), attention-based transformers, etc. Experience in current large language models (LLM) for representative and generative tasks.

Qualifications:

Bachelor's degree plus 11-years of relevant experience or equivalent. Proficiency with object-oriented languages (JAVA, Python) and experience with Jupyter Notebooks

Security Clearance: Active TS/SCI with Polygraph required (CCA) 


Nice To Have:

Proficiency in approaches to retrieval augmented generation (RAG), model context protocol (MCP) and other recent technologies supporting agentic AI. Foundational knowledge of statistics related to parametric probability distributions, nonparametric probability distributions, Bayesian analysis, and covariance matrices. Proficiency in agentic systems that can automatically carry out mission goals with limited supervision.

Salary Range: 207,000-227,000

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