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Bayesian Modeling Jobs in California (NOW HIRING)

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.

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.

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.

... e.g., bayesian pooling, hierarchical modeling) * Demonstrated communication skills and experience presenting complex findings to both technical and non-technical stakeholders * Demonstrated ...

... e.g., bayesian pooling, hierarchical modeling) * Demonstrated communication skills and experience presenting complex findings to both technical and non-technical stakeholders * Demonstrated ...

Data Science Lead

Milpitas, CA · On-site +1

$148K - $168K/yr

... Bayesian models; 6. Large volume healthcare datasets and data analysis workflows; 7. Scientific programming language Python or Julia; 8. Pandas Data processing library; 9. Process optimization tools ...

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Bayesian Modeling information

What is the difference between Bayesian Modeling vs Data Scientist?

AspectBayesian ModelingData Scientist
Required CredentialsStatistics, Mathematics, Data AnalysisStatistics, Computer Science, Data Analysis
Work EnvironmentResearch-focused, statistical modelingCross-functional, data analysis, visualization
Industry UsageResearch, academia, specialized analyticsBusiness, tech, finance, healthcare
Common Search/ComparisonYesYes

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 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.

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 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.
What cities in California are hiring for Bayesian Modeling jobs? Cities in California with the most Bayesian Modeling job openings:
Principal AI Scientist - AI Foundation

Principal AI Scientist - AI Foundation

Intuit

Mountain View, CA • On-site

$273K - $369K/yr

Full-time

Posted 5 days ago


Intuit rating

8.4

Company rating: 8.4 out of 10

Based on 90 frontline employees who took The Breakroom Quiz

77th of 217 rated software companies


Job description

Overview

Come join the AI Foundation as a Principal AI Scientist. The PDX AI Foundation team focuses on building capabilities that enable and accelerate AI development across Intuit. You will get the chance to lead the work to leverage the cutting-edge AI technologies at scale focusing on developing AI standards, Evaluation methodologies and tools, as well as collaborate across organizations and functions with our partners in the engineering, product management, analytics and design departments to ensure that we are accelerating the work of developers across Intuit


Responsibilities

  • Practices leadership and communication skills to influence teams and to evangelize data science across the organization
  • Collaborates with stakeholders to define success criteria and align model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products
  • Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team’s work, and holding the team accountable for high quality code, git, design, costs and implementation standards
  • Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them “model-ready”. You need to be willing and able to do your own ETL and design/build featurization.
  • 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 datasets.
  • Communicates with partners to ensure successful delivery and integration of DS solutions.
  • Proactively researches, explores, and enables new ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems.

Qualifications

  • 10+ years of industry experience with data science
  • BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent experience
  • 8+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.
  • Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization.
  • Proficient in NLP techniques, Explainable AI, and ML frameworks.
  • Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R.
  • Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
  • Quick learner, adaptable, with the ability to work independently in a fast-paced environment
  • Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users

Footer

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

The expected base pay range for this position is:
Mountain View $273,500 - $369,500

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