Experience with time-series forecasting, probabilistic forecasting, Bayesian modeling, extreme-value modeling, causal inference, stochastic processes, simulation, or uncertainty quantification
Experience with time-series forecasting, probabilistic forecasting, Bayesian modeling, extreme-value modeling, causal inference, stochastic processes, simulation, or uncertainty quantification
Research Scientist, Infrastructure Modeling and Reliability
Menlo Park, CA · On-site
$271K - $347K/yr
... Bayesian modeling, extreme-value modeling, causal inference, stochastic processes, simulation, or uncertainty quantification • Experience with infrastructure, capacity planning, power systems ...
Research Scientist, Infrastructure Modeling and Reliability
Menlo Park, CA · On-site
$271K - $347K/yr
... Bayesian modeling, extreme-value modeling, causal inference, stochastic processes, simulation, or uncertainty quantification • Experience with infrastructure, capacity planning, power systems ...
Data Scientist / Senior Data Scientist (Risk Modeling)
San Ramon, CA · On-site
$100K - $160K/yr
Bayesian methods * Hands-on experience with key programming tools: * Python (NumPy, pandas, scikit ... Modeling & Communication * Ability to translate complex statistical concepts into actionable ...
Quick apply
Data Scientist / Senior Data Scientist (Risk Modeling)
San Ramon, CA · On-site
$100K - $160K/yr
Bayesian methods * Hands-on experience with key programming tools: * Python (NumPy, pandas, scikit ... Modeling & Communication * Ability to translate complex statistical concepts into actionable ...
Preferred : • An understanding of machine learning concepts such as Neural Networks and Bayesian Models. • Experience in developing supervised and unsupervised learning algorithms. • ...
Preferred : • An understanding of machine learning concepts such as Neural Networks and Bayesian Models. • Experience in developing supervised and unsupervised learning algorithms. • ...
AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust ...
AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust ...
Proficiency across multiple ML domains: supervised and unsupervised learning, deep learning, time-series modeling, and Bayesian statistics. Production-quality software engineering in Python ...
Proficiency across multiple ML domains: supervised and unsupervised learning, deep learning, time-series modeling, and Bayesian statistics. Production-quality software engineering in Python ...
Data Scientist
San Jose, CA · On-site
Apply your expertise in designing, implementing and validating unsupervised deep learning, reinforcement learning and bayesian models. * Present exploratory findings to both, technical and management ...
Data Scientist
San Jose, CA · On-site
Apply your expertise in designing, implementing and validating unsupervised deep learning, reinforcement learning and bayesian models. * Present exploratory findings to both, technical and management ...
Experience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning concepts, or uncertainty quantification Experience ...
Experience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning concepts, or uncertainty quantification Experience ...
Experience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning concepts, or uncertainty quantification Experience ...
Experience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning concepts, or uncertainty quantification Experience ...
Data Scientist
San Jose, CA · On-site
Apply your expertise in designing, implementing and validating unsupervised deep learning, reinforcement learning and bayesian models. * Present exploratory findings to both, technical and management ...
Data Scientist
San Jose, CA · On-site
Apply your expertise in designing, implementing and validating unsupervised deep learning, reinforcement learning and bayesian models. * Present exploratory findings to both, technical and management ...
Develop highly scalable deep learning, reinforcement learning and bayesian models. * Support research projects by providing innovative designs for end-to-end Machine Learning systems. * Optimize ...
Develop highly scalable deep learning, reinforcement learning and bayesian models. * Support research projects by providing innovative designs for end-to-end Machine Learning systems. * Optimize ...
Develop highly scalable deep learning, reinforcement learning and bayesian models. * Support research projects by providing innovative designs for end-to-end Machine Learning systems. * Optimize ...
Develop highly scalable deep learning, reinforcement learning and bayesian models. * Support research projects by providing innovative designs for end-to-end Machine Learning systems. * Optimize ...
Staff AI Scientist
Mountain View, CA · On-site
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
Mountain View, CA · On-site
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
Mountain View, CA · On-site
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
Mountain View, CA · On-site
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Principal AI Scientist - AI Foundation
Mountain View, CA · On-site
$273.50 - $369.50/hr
Lead technical work of a Scrum team: initiating and designing model solutions, driving ... Bayesian learning, reinforcement learning, or deep learning) to real‑world problems and datasets.
Principal AI Scientist - AI Foundation
Mountain View, CA · On-site
$273.50 - $369.50/hr
Lead technical work of a Scrum team: initiating and designing model solutions, driving ... Bayesian learning, reinforcement learning, or deep learning) to real‑world problems and datasets.
Principal AI Scientist - AI Foundation
$273K - $369K/yr
Collaborates with stakeholders to define success criteria and align model metrics with business ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Principal AI Scientist - AI Foundation
$273K - $369K/yr
Collaborates with stakeholders to define success criteria and align model metrics with business ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
Mountain View, CA · On-site
$150 - $190/hr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
Mountain View, CA · On-site
$150 - $190/hr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Principal AI Scientist - AI Foundation
Mountain View, CA · On-site
$273K - $369K/yr
Collaborates with stakeholders to define success criteria and align model metrics with business ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Principal AI Scientist - AI Foundation
Mountain View, CA · On-site
$273K - $369K/yr
Collaborates with stakeholders to define success criteria and align model metrics with business ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
Mountain View, CA · On-site
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
Mountain View, CA · On-site
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Staff AI Scientist
$209K - $283K/yr
In this role you will be building and deploying machine learning models using both analytical ... Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets.
Bayesian Modeling information
See Santa Clara, CA salary details
$12.14 - $19.92
4% of jobs
$19.92 - $27.69
1% of jobs
$27.69 - $35.47
2% of jobs
$35.47 - $43.25
0% of jobs
$43.25 - $51.02
3% of jobs
$51.02 - $58.80
8% of jobs
$61.59 is the 25th percentile. Wages below this are outliers.
$58.80 - $66.58
17% of jobs
The median wage is $71.14 / hr.
$66.58 - $74.35
24% of jobs
$79.39 is the 75th percentile. Wages above this are outliers.
$74.35 - $82.13
23% of jobs
$82.13 - $89.90
12% of jobs
$89.90 - $97.68
5% of jobs
$12
$68
$97
How much do bayesian modeling jobs pay per hour?
As of Sep 3, 2026, the average hourly pay for bayesian modeling in Santa Clara, CA is $68.95, according to ZipRecruiter salary data. Most workers in this role earn between $61.83 and $80.19 per hour, depending on experience, location, and employer.
What is Bayesian modeling?
Bayesian modeling is a statistical approach that uses Bayes' Theorem to update the probability of a hypothesis as more data becomes available. It incorporates prior beliefs or knowledge, combines them with observed data, and produces a posterior probability distribution to guide inference and decision-making. This approach is widely used in various fields such as machine learning, data science, and scientific research for tasks like parameter estimation, prediction, and model selection.
How does a Bayesian modeling specialist typically collaborate with cross-functional teams in a workplace setting?
Bayesian Modeling specialists often work closely with data scientists, software engineers, and domain experts to integrate probabilistic models into larger analytical or production systems. They are involved in translating complex statistical concepts into actionable insights and recommendations tailored to business needs. Effective communication is key, as they must present findings to both technical and non-technical stakeholders, ensuring that model assumptions and results are clearly understood. Collaboration may also include contributing to code reviews, sharing best practices for model validation, and mentoring colleagues on Bayesian methodologies.
What are the key skills and qualifications needed to thrive as a Bayesian modeler, and why are they important?
To thrive as a Bayesian Modeler, you need a solid background in statistics, probability theory, and mathematical modeling, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as R, Python, or Stan, and experience with statistical software and Bayesian inference tools are essential. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting results and collaborating with multidisciplinary teams. These skills ensure accurate model development, reliable data-driven insights, and clear communication of complex findings to stakeholders.
What is the difference between Bayesian Modeling vs Data Scientist?
| Aspect | Bayesian Modeling | Data Scientist |
|---|---|---|
| Required Credentials | Statistics, Mathematics, Data Analysis | Statistics, Computer Science, Data Analysis |
| Work Environment | Research-focused, statistical modeling | Cross-functional, data analysis, visualization |
| Industry Usage | Research, academia, specialized analytics | Business, tech, finance, healthcare |
| Common Search/Comparison | Yes | Yes |
Bayesian Modeling and Data Scientists often overlap in skills like statistics and data analysis. Bayesian Modeling specializes in probabilistic models and statistical inference, while Data Scientists have broader roles including data cleaning, visualization, and machine learning. Both roles are essential in data-driven industries, but Bayesian Modeling is more focused on advanced statistical techniques.
What are popular job titles related to Bayesian Modeling jobs in Santa Clara, CA?
For Bayesian Modeling jobs in Santa Clara, CA, the most frequently searched job titles are:
- Contract Machine Learning Engineer
- Machine Learning Engineer Apprenticeship
- Llm Ml Rag
- Urgently Hiring Machine Learning Engineer New Grad
- Volunteer Large Language Model Llm
- Manager Edge Ai Machine Learning
- Deep Learning Engineer
- Temporary Machine Learning R
- Senior Machine Learning Engineer
- Machine Learning Ai Developer
What job categories do people searching Bayesian Modeling jobs in Santa Clara, CA look for?
The top searched job categories for Bayesian Modeling jobs in Santa Clara, CA are:
- Physics Informed Machine Learning
- Staff Machine Learning Engineer
- No Experience Nvidia Machine Learning
- Artificial Intelligence Machine Learning Engineer
- Contract Apple Machine Learning Engineer
- Founding Data Engineer
- Machine Learning Manager
- Bayesian Optimization
- Associate Machine Learning Chemistry
- Machine Learning Engineer Two
$271K/yr
Full-time
Posted 24 days ago
Meta rating
7.8
Based on 45 frontline employees who took The Breakroom Quiz
138th of 247 rated software companies
Job description
Meta builds technologies that help people connect, find communities, and grow businesses. Meta’s infrastructure supports services used by billions of people, and operating that infrastructure efficiently requires increasingly sophisticated modeling of demand, utilization, reliability, and physical resource constraints.We are seeking an industry-leading Research Scientist or Applied Scientist to define and build new modeling approaches for power utilization across Meta’s infrastructure. This role will lead the development of statistical and machine learning models that monitor power consumption, project peak demand, quantify uncertainty, and inform how Meta maximizes usable power within failure domains while maintaining target reliability levels. The ideal candidate has deep experience modeling high-dimensional, noisy, and interdependent systems, and has demonstrated the ability to translate scientific advances into production systems that influence large-scale infrastructure strategy.
Research Scientist, Infrastructure Modeling and Reliability Responsibilities:
Minimum Qualifications:
Preferred Qualifications:
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Research Scientist, Infrastructure Modeling and Reliability Responsibilities:
- Define the scientific and technical strategy for modeling power consumption, peak risk, and reliability tradeoffs across large-scale infrastructure systems.
- Develop statistical, machine learning, and/or optimization models that forecast power demand, estimate peak distributions, quantify uncertainty, and support operational decision-making.
- Build approaches that reason about high-dimensional signals, correlated demand, failure-domain constraints, reserve margins, and reliability targets.
- Partner with engineering, capacity planning, data center, energy, hardware, operations, and finance teams to translate model outputs into infrastructure planning and utilization decisions.
- Establish evaluation frameworks, backtesting methods, confidence intervals, and monitoring systems to measure model quality and operational risk.
- Identify opportunities to safely increase power utilization, reduce stranded capacity, improve cost efficiency, and guide long-term infrastructure investment.
- Lead ambiguous, company-critical technical initiatives across organizations, influencing strategy and aligning stakeholders around scientifically grounded decisions.
- Mentor senior scientists and engineers, raise the technical bar for modeling and forecasting systems, and represent Meta’s work through appropriate external publications, talks, or industry engagement.
Minimum Qualifications:
- 10+ years of experience developing statistical, machine learning, simulation, forecasting, optimization, or other quantitative modeling systems
- Experience leading ambiguous, cross-functional technical programs from problem definition through model development, evaluation, deployment, and business impact
- Experience coding in Python, R, C++, Java, or similar languages for data analysis, modeling, simulation, or production systems
- Experience communicating complex technical concepts, assumptions, uncertainty, and tradeoffs to technical and non-technical audiences
- Experience influencing technical strategy across multiple teams or organizations
- PhD in Computer Science, Statistics, Applied Mathematics, Operations Research, Electrical Engineering, Physics, Economics, or a related quantitative field, or equivalent practical experience
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Preferred Qualifications:
- Experience modeling high-dimensional, sparse, noisy, or strongly correlated data in production environments
- Experience with time-series forecasting, probabilistic forecasting, Bayesian modeling, extreme-value modeling, causal inference, stochastic processes, simulation, or uncertainty quantification
- Experience with infrastructure, capacity planning, power systems, energy systems, data centers, reliability engineering, distributed systems, supply-chain optimization, or resource allocation
- Experience building models that support operational decisions under explicit reliability, safety, cost, or utilization constraints
- Experience developing peak-demand forecasts, confidence intervals, risk estimates, anomaly detection, or backtesting frameworks
- Experience applying optimization, operations research, or decision science to large-scale resource planning
- Demonstrated record of industry-level technical leadership, such as defining new research directions, influencing company strategy, publishing in leading venues, or shaping external technical standards
- Experience mentoring senior technical contributors and building scientific communities across organizations
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$271,000/year to $347,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$271,000/year to $347,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
About Meta
Sourced by ZipRecruiter
Industry
Internet and it, media and telecom and software development
Company size
10,000+ Employees
Headquarters location
Menlo Park, CA, US