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Bayesian Jobs in California (NOW HIRING)

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 ...

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 ...

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 ...

Staff AI Scientist

Mountain View, CA · 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 ...

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 ...

Staff AI Scientist

Mountain View, CA · 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 ...

Staff AI Scientist

Mountain View, CA · 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 ...

Staff AI Scientist

Mountain View, CA · 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 ...

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 ...

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Showing results 1-20

Bayesian information

See California salary details

$146.4K

$156.7K

$167.9K

How much do bayesian jobs pay per year?

As of Aug 12, 2026, the average yearly pay for bayesian in California is $156,676.00, according to ZipRecruiter salary data. Most workers in this role earn between $151,780.00 and $161,572.00 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 California? The most popular types of Bayesian jobs in California are:
What job categories do people searching Bayesian jobs in California look for? The top searched job categories for Bayesian jobs in California are:
What cities in California are hiring for Bayesian jobs? Cities in California with the most Bayesian job openings:
Infographic showing various Bayesian job openings in California as of August 2026, with employment types broken down into 89% Full Time, and 11% Part Time. Highlights an 84% In-person, and 16% Remote job distribution, with an average salary of $156,676 per year, or $75.3 per hour.

Bayesian Statistics Expert - Problem Designer

Mercor

San Francisco, CA • Remote

$70 - $100/hr

Full-time

Re-posted 12 days ago


Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Computational Bayesian Statistics and Applied Mathematics Expert
Type: Contract
Compensation: $70–$100/hour
Location: Remote
Commitment: 15–20 hours/week

Role Responsibilities

  • Design challenging computational problems to test AI capabilities in solving scientific and engineering tasks.
  • Develop problems requiring the use of specialized scientific software for simulations and experiment design.
  • Refine problems through iterative testing against state-of-the-art AI models to achieve target difficulty.
  • Collaborate on creating tasks that require strategic thinking and efficient information extraction.
  • Work independently to enhance problem designs based on feedback and model performance.

Qualifications

Must-Have

  • Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
  • Proven proficiency with at least one scientific software library (PyMC, FEniCS, etc.).
  • Strong Python skills for writing problem setups and solution validators.
  • Ability to work independently and refine problem designs.
  • Comfortable in a Linux/terminal environment with remote compute sandboxes.
  • Available for at least 15–20 hours/week.

Preferred

  • Experience across multiple domains or tools.
  • Familiarity with benchmark or evaluation design.
  • Background in scientific teaching or exam/problem-set design.
  • Experience with computational reproducibility and containerized environments.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.