The individual will develop a machine learning and Bayesian statistics-based approach to model assay variability using medium to high throughput screening datasets. The individual will work in a ...
The individual will develop a machine learning and Bayesian statistics-based approach to model assay variability using medium to high throughput screening datasets. The individual will work in a ...
Access to proprietary datasets, high-performance compute, and Lila's research infrastructure Research areas include ML-accelerated simulations, Bayesian methods, representation learning, generative ...
Access to proprietary datasets, high-performance compute, and Lila's research infrastructure Research areas include ML-accelerated simulations, Bayesian methods, representation learning, generative ...
Postdoctoral Fellow - Biostatistics
Houston, TX · On-site
$64K - $76K/yr
The primary research focus will be on developing novel statistical methodologies and software for Bayesian adaptive clinical trial designs. The postdoctoral fellow will also actively participate in ...
Postdoctoral Fellow - Biostatistics
Houston, TX · On-site
$64K - $76K/yr
The primary research focus will be on developing novel statistical methodologies and software for Bayesian adaptive clinical trial designs. The postdoctoral fellow will also actively participate in ...
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
Quick apply
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
Postdoctoral Fellow - Biostatistics
Houston, TX · On-site +1
$64K - $76K/yr
The primary research focus will be on developing novel statistical methodologies and software for Bayesian adaptive clinical trial designs. The postdoctoral fellow will also actively participate in ...
Postdoctoral Fellow - Biostatistics
Houston, TX · On-site +1
$64K - $76K/yr
The primary research focus will be on developing novel statistical methodologies and software for Bayesian adaptive clinical trial designs. The postdoctoral fellow will also actively participate in ...
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
Digital Innovation Engineer
Wilmington, DE · On-site
$132K/yr
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
Digital Innovation Engineer
Wilmington, DE · On-site
$132K/yr
This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in ...
... and Bayesian Optimization to solve pricing and optimization challenges AI Agents for Pricing Build AIdriven pricing agents that incorporate consumer behaviour demand elasticity and competitive ...
... and Bayesian Optimization to solve pricing and optimization challenges AI Agents for Pricing Build AIdriven pricing agents that incorporate consumer behaviour demand elasticity and competitive ...
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
Atlanta, GA · On-site
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
Atlanta, GA · On-site
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 ...
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 ...
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 ...
PyMC, Stan, or similar Bayesian libraries * Engineering & CI/CD * DevOps: Git, Docker, CI/CD pipelines, Airflow
New
PyMC, Stan, or similar Bayesian libraries * Engineering & CI/CD * DevOps: Git, Docker, CI/CD pipelines, Airflow
New
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 ...
Senior Staff AI Scientist
Atlanta, GA · On-site
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 ...
Senior Staff AI Scientist
Atlanta, GA · On-site
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 ...
Sr. Analyst, Product Analytics
San Mateo, CA · On-site +1
$175K - $248K/yr
... Bayesian experimentation; designing multi-dimensional test structures and interpreting results across user segments; mitigating validity threats including SRM, contamination, and multi-exposure ...
Sr. Analyst, Product Analytics
San Mateo, CA · On-site +1
$175K - $248K/yr
... Bayesian experimentation; designing multi-dimensional test structures and interpreting results across user segments; mitigating validity threats including SRM, contamination, and multi-exposure ...
Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and decision-making. * Utilize Markov chains and stochastic processes for modeling sequential or ...
Quick apply
Design and implement Bayesian models and probabilistic frameworks for uncertainty estimation and decision-making. * Utilize Markov chains and stochastic processes for modeling sequential or ...
Postdoctoral Fellow - Biostatistics
Houston, TX · On-site
$64K - $76K/yr
The primary focus will be to develop novel methods for causal AI/inference methods, adaptive Bayesian clinical trial designs, derive related statistical theory, produce software for implementation ...
Postdoctoral Fellow - Biostatistics
Houston, TX · On-site
$64K - $76K/yr
The primary focus will be to develop novel methods for causal AI/inference methods, adaptive Bayesian clinical trial designs, derive related statistical theory, produce software for implementation ...
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 ...
Bayesian information
See salary details
$149.5K - $151.5K
7% of jobs
$151.5K - $153.5K
10% of jobs
$155.2K is the 25th percentile. Wages below this are outliers.
$153.5K - $155.5K
10% of jobs
$155.5K - $157.5K
10% of jobs
$157.5K - $159.5K
10% of jobs
The median wage is $160.3K / yr.
$159.5K - $161.5K
10% of jobs
$161.5K - $163.5K
10% of jobs
$165.3K is the 75th percentile. Wages above this are outliers.
$163.5K - $165.5K
10% of jobs
$165.5K - $167.5K
10% of jobs
$167.5K - $169.5K
10% of jobs
$169.5K - $171.5K
4% of jobs
$149.5K
$160K
$171.5K
How much do bayesian jobs pay per year?
As of Jun 9, 2026, the average yearly pay for bayesian in the United States is $159,999.00, according to ZipRecruiter salary data. Most workers in this role earn between $155,000.00 and $165,000.00 per year, depending on experience, location, and employer.
What are the typical projects or challenges faced in a Bayesian-focused 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 job?
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 are the key skills and qualifications needed to thrive in the Bayesian position, 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.
More about Bayesian jobs
What cities are hiring for Bayesian jobs? Cities with the most Bayesian job openings:
What are the most commonly searched types of Bayesian jobs? The most popular types of Bayesian jobs are:
What states have the most Bayesian jobs? States with the most job openings for Bayesian jobs include:
What job categories do people searching Bayesian jobs look for? The top searched job categories for Bayesian jobs are:
Full-time
Posted 6 days ago
Job description
Scientific - Data Scientist
Location - Lawrenceville, New Jersey 08648
Role is 100% onsite.
Junior (0-3 Yrs.)
Description
Leads discovery and optimization (LDO) is a diverse group of scientists and engineers, providing critical assay information to therapeutic research centers (TRCs) throughout research and early development (R&ED). We are seeking a highly motivated and innovative data scientist to join the data science and advanced analytics team within LDO until the end of 2023. The individual will develop a machine learning and Bayesian statistics-based approach to model assay variability using medium to high throughput screening datasets. The individual will work in a highly dynamic environment at the center of the R&ED drug discovery engine to develop cutting edge tools applied to complex drug discovery problems.
Roles and Responsibilities
• Write python scripts to enable rapid cleaning and analysis of medium and high throughput datasets
• Utilize machine learning (ML) approaches to generate small molecules features
• Utilize Bayesian statistics approaches to estimate uncertainties in assay datasets, based on results on above ML outputs
• Write and document programming code (python preferred) to facilitate data preparation / cleaning, model development, and evaluation
• Produce high quality scripts, documentation, and processing pipeline by the end of 2023
• Create deployable version of processing pipeline for near term use as a stand-alone application and ultimately future integration with enterprise suite
Qualifications
• Ph.D. in quantitative sciences/engineering (computer science, mathematics, statistics, or engineering)
• 5+ years of relevant professional experience with a proven track record in machine learning and data science - experience in drug discovery machine learning is desirable but not required
• Strong knowledge of one or more scripting programming languages, with a focus on machine learning (e.g., Python (preferred), R, Matlab, C/C++)
• Experience utilizing molecular features of small molecules in machine learning models
• Experience with the use and application of Bayesian statistics and simulation methods in generating probabilistic outcomes
• Able to extract information from databases using a variety of software packages (e.g., Oracle SQL developer)
• Ability to build and maintain databases aligned with enterprise solutions is desirable but not required
• Strong analytical and problem solving skills to understand technical business problems and implement solutions
• Ability to work effectively on matrixed teams to collaboratively solve challenging problems, while also able to work independently with minimal resources
• Has good interpersonal, communication, writing and organizational skills
• Strong preference for on-site presence to enable colocation with data science team
Location - Lawrenceville, New Jersey 08648
Role is 100% onsite.
Junior (0-3 Yrs.)
Description
Leads discovery and optimization (LDO) is a diverse group of scientists and engineers, providing critical assay information to therapeutic research centers (TRCs) throughout research and early development (R&ED). We are seeking a highly motivated and innovative data scientist to join the data science and advanced analytics team within LDO until the end of 2023. The individual will develop a machine learning and Bayesian statistics-based approach to model assay variability using medium to high throughput screening datasets. The individual will work in a highly dynamic environment at the center of the R&ED drug discovery engine to develop cutting edge tools applied to complex drug discovery problems.
Roles and Responsibilities
• Write python scripts to enable rapid cleaning and analysis of medium and high throughput datasets
• Utilize machine learning (ML) approaches to generate small molecules features
• Utilize Bayesian statistics approaches to estimate uncertainties in assay datasets, based on results on above ML outputs
• Write and document programming code (python preferred) to facilitate data preparation / cleaning, model development, and evaluation
• Produce high quality scripts, documentation, and processing pipeline by the end of 2023
• Create deployable version of processing pipeline for near term use as a stand-alone application and ultimately future integration with enterprise suite
Qualifications
• Ph.D. in quantitative sciences/engineering (computer science, mathematics, statistics, or engineering)
• 5+ years of relevant professional experience with a proven track record in machine learning and data science - experience in drug discovery machine learning is desirable but not required
• Strong knowledge of one or more scripting programming languages, with a focus on machine learning (e.g., Python (preferred), R, Matlab, C/C++)
• Experience utilizing molecular features of small molecules in machine learning models
• Experience with the use and application of Bayesian statistics and simulation methods in generating probabilistic outcomes
• Able to extract information from databases using a variety of software packages (e.g., Oracle SQL developer)
• Ability to build and maintain databases aligned with enterprise solutions is desirable but not required
• Strong analytical and problem solving skills to understand technical business problems and implement solutions
• Ability to work effectively on matrixed teams to collaboratively solve challenging problems, while also able to work independently with minimal resources
• Has good interpersonal, communication, writing and organizational skills
• Strong preference for on-site presence to enable colocation with data science team