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Bayesian Statistics Jobs (NOW HIRING)

MORSE Corp

Bayesian Data Scientist

Cambridge, MA · On-site

$90K - $210K/yr

MORSE is searching for an experienced Data Scientist with expertise in Bayesian statistics, probabilistic modeling, data analysis, data science, and algorithm development in one or more of a variety ...

Think Tank, Inc.

Statistician

Seattle, WA · On-site

Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred.Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience ...

Exponent

Statistical Scientist (Ph.D.)

Bellevue, WA · On-site

... Bayesian statistics, machine learning, or quality control/improvement • Minimum of 2 years' experience with one or more statistical software packages (SAS and R are preferred) • Ability to work ...

Think Tank, Inc.

Statistician

Seattle, WA · On-site

Knowledge and experience in Bayesian statistics and mark-recapture methods is strongly preferred. * Advanced degree in a related field - may substitute for two (MS) or four (PhD) years of experience ...

Exponent Inc.

Statistical Scientist (Ph.D.)

Bellevue, WA · On-site

$133 - $150/hr

... Bayesian statistics, machine learning, or quality control/improvement. * Minimum of 2 years experience with statistical software packages such as SAS and R. * Ability to work independently and in ...

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How much do bayesian statistics jobs pay per year?

As of Aug 22, 2026, the average yearly pay for bayesian statistics in the United States is $90,119.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,500.00 and $106,500.00 per year, depending on experience, location, and employer.

What is Bayesian statistics?

A Bayesian Statistics job involves using Bayesian methods to analyze data, update probabilities, and make inferences based on prior knowledge. Professionals in this field apply Bayesian techniques in areas like machine learning, finance, healthcare, and scientific research. They typically work with probabilistic models, statistical software, and programming languages such as Python or R. These roles require strong mathematical skills and are often found in academia, industry, and government research.

What does a typical day look like for someone working in Bayesian statistics?

A typical day for a professional specializing in Bayesian Statistics often involves designing and running statistical models, analyzing datasets using Bayesian methods, and programming in tools like R or Python. You may collaborate with data scientists, researchers, and subject matter experts to define problems and interpret statistical results. Responsibilities can also include presenting findings to non-technical stakeholders, developing new modeling techniques, and staying updated with advances in Bayesian methodology. The role offers a dynamic mix of technical analysis, problem-solving, and teamwork, making each day intellectually engaging.

What are the key skills and qualifications needed to thrive in the Bayesian statistics position, and why are they important?

To thrive in Bayesian Statistics, you need a deep understanding of probability theory, statistical modeling, and strong programming skills, usually supported by an advanced degree in statistics, mathematics, or a related field. Familiarity with technical tools like R, Python, Stan, and software for Bayesian inference, as well as relevant certifications, is often required. Analytical thinking, attention to detail, and the ability to clearly communicate complex concepts are essential soft skills. These skills and qualities ensure accurate and interpretable statistical analyses, effective collaboration with cross-functional teams, and reliable data-driven decision making.

What can you do with Bayesian statistics?

A professional in Bayesian statistics applies probabilistic models to analyze data, make predictions, and update beliefs based on new information. This skill is used in fields like data science, machine learning, and research to improve decision-making and model uncertainty. Proficiency in statistical software such as R or Python is often required.

What is a Bayesian statistician?

A Bayesian statistician is a professional who applies Bayesian methods to analyze data, update probabilities, and make statistical inferences. They often use tools like statistical software and require strong knowledge of probability theory and modeling techniques to interpret data within a Bayesian framework.

What jobs use Bayesian statistics?

Jobs that use Bayesian statistics include data scientists, statisticians, machine learning engineers, and quantitative analysts. These roles often involve developing probabilistic models, analyzing data, and making predictions using Bayesian methods and tools like R or Python. Strong analytical skills and knowledge of statistical software are typically required.
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What are the most commonly searched types of Bayesian Statistics jobs?

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What states have the most Bayesian Statistics jobs?

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Bayesian Statistics jobs near you
Infographic showing various Bayesian Statistics job openings in the United States as of August 2026, with employment types broken down into 79% Full Time, 19% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $90,119 per year, or $43.3 per hour.

Member of Technical Staff, Bayesian Statistics

Ataraxis AI

New York, NY • On-site

$100K - $300K/yr

Full-time

Posted 11 days ago

Job description

About Ataraxis AI
Ataraxis is a clinical AI research lab working at the intersection of multi-modal AI and precision medicine. Our goal is to make disease predictable. To accomplish this, we develop new AI methods that predict patient outcomes and treatment response, and build clinical tools to assist physicians in selecting the most optimal treatments for their patients.
Our AI research lab discovers and develops methods to recognize patterns and predict outcomes across complex, multi-modal clinical data. This spans our causality (Ataraxis™ Tau), foundation model (Falcon and Kestrel for digital pathology), and survival analysis research.
Our first clinical products, such as Ataraxis™ Breast for breast cancer, already help patients get the most appropriate treatment across the best academic institutions and community clinics worldwide.
At Ataraxis, you will have a unique opportunity to shape not only the future of our company, but also the future of healthcare. You will join an exceptional team at the forefront of clinical AI research and deployment. Our advisors include AI pioneers such as our founding advisor, Yann LeCun, and distinguished oncologists from top cancer research institutions, all united by the mission to redefine precision medicine.
Ataraxis has raised over $24 million in funding, including a $20 million Series A led by top venture capital funds such as Thiel Capital/Founders Fund (OpenAI, SpaceX, Palantir), Obvious Ventures (AMI Labs, Inceptive, Radical Numerics, Recursion), and AIX Ventures (Hugging Face, Perplexity).
We are an company with a flat organizational structure, where every team member is empowered to actively contribute. Leadership roles are earned by those who demonstrate initiative and consistently deliver exceptional results. Strong work ethic and the ability to prioritize ruthlessly are essential.
Responsibilities
  • Design and implement novel Bayesian statistics methods.
  • Translate machine learning papers into production-ready code.
  • Build robust model evaluation frameworks.
  • Disseminate the results by co-authoring research papers and abstracts.
  • Collaborate with a multidisciplinary team of engineers and scientists.
  • Co-mentor junior members of the team.
Qualifications
  • PhD degree in statistics or machine learning.
  • Excellent knowledge of Bayesian statistics, including Gaussian processes and Bayesian clinical trial design.
  • Passion for research, attention to detail and ability to drive tasks to completion. Strong preference will be given to candidates with papers in A* conferences (e.g. ICML, ICLR, NeurIPS, CVPR) or top-tier statistics journals.
  • Excellent understanding of core machine learning concepts.
  • Excellent knowledge of the foundations of statistics, linear algebra, probability and machine learning.
  • Excellent skills in Python and PyTorch.
  • Experience with applying Bayesian statistics to uncertainty quantification in deep learning and model explainability.
  • Experience in deep learning. Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability and computational pathology is a bonus.

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