ZipRecruiter

Log In

1

Bayesian Networks Jobs in Boston, MA (NOW HIRING)

Extropic

Research Scientist - Machine Learning

Boston, MA · On-site

$150K - $250K/yr

... networks (GNNs) * Strong theoretical background in information geometry * Strong theoretical background in random matrix theory * Strong grasp of computational Bayesian methods, including MCMC ...

Roberts Recruiting

Senior Data Scientist

Boston, MA · On-site

... networks). * Understand Bayesian modeling techniques. * Are capable of analyzing data and making rigorous statements about what can or cannot be concluded. * Have experience designing and ...

Ameriprise Financial

Senior Data Scientist

Boston, MA · On-site

Competence using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar ...

Ameriprise Financial

Senior Data Scientist

Boston, MA · On-site

Competence using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar ...

People also search for

All JobsBayesian Networks JobsBayesian Networks Jobs in Boston, MA

Bayesian Networks information

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.

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 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 popular job titles related to Bayesian Networks jobs in Boston, MA? For Bayesian Networks jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Bayesian Networks jobs in Boston, MA look for? The top searched job categories for Bayesian Networks jobs in Boston, MA are:
Bayesian Networks jobs near you
Infographic showing various Bayesian Networks job openings in Boston, MA as of May 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 18% Physical, 27% Hybrid, and 55% Remote job distribution.

Research Scientist - Machine Learning

Extropic

Boston, MA • On-site

$150K - $250K/yr

Full-time

Posted yesterday

Job description

Overview
Extropic's hardware massively accelerates certain kinds of probabilistic inference.  Our ML team works on the science of training models in the thermodynamic paradigm, and we are looking for senior research and engineering talent to derive probabilistic ML theory, empirically demonstrate its scaling properties, and deploy performant models. Senior hires will be leading their own research direction and are therefore expected to quickly become experts across our abstraction stack, including the hardware, software, physics, and math.
Responsibilities
  • Collaborate with senior researchers, residents, engineers, and physicists to derive the theory of new probabilistic models and their learning rules, including energy-based models and diffusion models.
  • Scale up experimentation infrastructure and optimize over the design space of models.
  • Implement, visualize, and evaluate new architectures, training algorithms, and benchmarks.
  • Publish papers, contribute to open source, and communicate design insights to our hardware team.
  • Create production models for domain experts using customer data.
Required Qualifications
  • Experience in scientific Python and at least one deep learning framework (PyTorch, JAX, TensorFlow, Keras)
  • Extremely strong foundations in probability and linear algebra
  • Familiarity with deep learning theory and literature, including theory of over-parameterization and scaling laws
  • Publications in top ML conferences (NeurIPS, ICML, ICLR, CVPR)
  • Experience training high-performance models, including familiarity with infrastructure (Slurm, Ray, Weights & Biases)
  • Experience deploying models, including familiarity with infrastructure (Ray, AWS, ONNX)
Preferred Qualifications
  • Experience designing probabilistic graphical models (PGM)
  • Experience training energy-based models (EBMs) or diffusion models
  • Experience with numerical methods in diffeq solvers
  • Experience with message passing or training graph neural networks (GNNs)
  • Strong theoretical background in information geometry
  • Strong theoretical background in random matrix theory
  • Strong grasp of computational Bayesian methods, including MCMC sampling methods and variational inference
$150,000 - $250,000 a year
Salary and equity compensation will vary with experience
 
Extropic is an equal opportunity employer
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
apply for this job

Apply