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Bayesian Jobs in New York (NOW HIRING)

Meta

Competitive Intelligence Lead

New York, NY

$177K/yr

Synthesize multiple, low-fidelity 3rd-party signals into a single high-fidelity trend report using Bayesian aggregation or other methods * Data transformation: Apply quasi-experimental designs (e.g ...

Intuit

Staff AI Scientist

Manhattan, NY · On-site

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

Intuit

Staff AI Scientist

Manhattan, NY

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

Intuit

Staff AI Scientist

New York, NY · On-site

$209K - $283K/yr

Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and ...

CyberCoders

Data Scientist

New York, NY

Experience with statistical methods and analysis such as bias vs. variance tradeoffs, Bayesian Stats, probability distributions, regressions, etc. Coding experience in a production environment. You ...

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Bayesian information

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$163.4K

$174.9K

$187.5K

How much do bayesian jobs pay per year?

As of Aug 17, 2026, the average yearly pay for bayesian in New York is $174,923.00, according to ZipRecruiter salary data. Most workers in this role earn between $169,457.00 and $180,389.00 per year, depending on experience, location, and employer.

What are the typical projects or challenges faced in a Bayesian role?

In a Bayesian role, you’ll often work on projects involving probabilistic modeling, uncertainty quantification, and predictive analytics for real-world decision-making. Common challenges include structuring prior distributions, ensuring computational efficiency for complex models, and clearly explaining Bayesian results to non-technical stakeholders. You might collaborate closely with data engineers, domain experts, and business analysts to refine models and translate findings into actionable recommendations. This role offers the opportunity to tackle diverse analytical problems across industries like healthcare, finance, or tech, supporting ongoing professional growth and learning.

What is a Bayesian?

A Bayesian job typically involves applying Bayesian statistics, probabilistic modeling, and inference techniques to analyze data and make decisions under uncertainty. Professionals in this field use Bayes' theorem to update beliefs based on new evidence, often working in areas like machine learning, finance, healthcare, and research. Common roles include Bayesian statisticians, data scientists, and researchers who build probabilistic models to improve predictions and decision-making.

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, updating beliefs with new data, and using tools like R or Python for analysis. Bayesian methods are common in fields such as finance, healthcare, and research for decision-making and predictive modeling.

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

To thrive as a Bayesian (typically a Bayesian Data Scientist or Statistician), you need a strong background in probability theory, statistical modeling, and mathematics, often with an advanced degree in statistics, data science, or a related quantitative field. Experience with programming languages such as Python or R, Bayesian analysis libraries (e.g., Stan, PyMC), and familiarity with statistical software are commonly required. Analytical thinking, collaborative teamwork, and the ability to communicate complex results clearly are valuable soft skills in this role. These abilities are essential for designing robust models, interpreting data accurately, and delivering actionable insights to interdisciplinary teams.

What are the most commonly searched types of Bayesian jobs in New York?

The most popular types of Bayesian jobs in New York are:

What are popular job titles related to Bayesian jobs in New York?

For Bayesian jobs in New York, the most frequently searched job titles are:

What job categories do people searching Bayesian jobs in New York look for?

The top searched job categories for Bayesian jobs in New York are:

What cities in New York are hiring for Bayesian jobs?

Cities in New York with the most Bayesian job openings:

Bayesian jobs near you
Infographic showing various Bayesian job openings in New York as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 63% Physical, 3% Hybrid, and 34% Remote job distribution, with an average salary of $174,923 per year, or $84.1 per hour.

Member of Technical Staff, Bayesian Statistics

Ataraxis AI

New York, NY • On-site

$100K - $300K/yr

Full-time

Posted 6 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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