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Bayesian Phd Jobs (NOW HIRING)

Intuit

Staff AI Scientist

Atlanta, GA · On-site

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Staff AI Scientist

Atlanta, GA · On-site

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Senior Staff AI Scientist

Atlanta, GA · On-site

Qualifications * 6+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Senior Staff AI Scientist

Atlanta, GA

Qualifications * 6+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Euro Math Soc

NY · On-site

$70 - $90/hr

... PhD students. The department has developed several courses within data science, e.g., Bayesian methods, Advanced Machine Learning, Deep Learning and AI methods. You are expected to teach these types ...

New

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Staff AI Scientist

Manhattan, NY · On-site

$209K - $283K/yr

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

Intuit

Staff AI Scientist

Mountain View, CA

$209K - $283K/yr

Qualifications * 4+ years of industry experience with AI science * BS, MS or PhD in Statistics ... Bayesian optimization. * Proficient in NLP techniques, Explainable AI, and ML frameworks.

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Bayesian Phd information

What is a Bayesian PhD?

A Bayesian PhD typically refers to an individual who has completed a doctoral program with a focus on Bayesian statistics or Bayesian methods in their research. Bayesian statistics is a branch of statistics that uses probability distributions to represent uncertainty about unknowns, updating beliefs as new data becomes available. Students in this field learn to develop and apply Bayesian models to a wide range of problems in science, engineering, and social sciences. A PhD program with a Bayesian focus often involves advanced coursework in probability theory, statistical inference, and computational methods, as well as original research using Bayesian approaches.

What are the key skills and qualifications needed to thrive as a Bayesian PhD?

To thrive as a Bayesian PhD, you need advanced knowledge of probability theory, statistical inference, and mathematics, typically supported by a doctoral degree in statistics, mathematics, or a related field. Proficiency with statistical programming languages like R, Python, and specialized Bayesian tools such as Stan or BUGS is essential. Strong critical thinking, problem-solving, and clear communication skills help in articulating complex analyses and collaborating across disciplines. These capabilities are crucial for developing rigorous models, conducting impactful research, and translating statistical insights into actionable solutions.

What are some common challenges faced by a Bayesian PhD researcher during collaborative projects?

Bayesian PhD researchers often collaborate with interdisciplinary teams, which can present challenges such as communicating complex statistical concepts to non-specialists and integrating Bayesian methods with other analytical frameworks. Balancing the depth of theoretical work with practical problem-solving, managing computational demands, and aligning project goals with collaborators' expectations are also common hurdles. Successful collaboration typically requires strong communication skills, adaptability, and a willingness to bridge methodological gaps between disciplines.

What is the difference between Bayesian Phd vs Data Scientist?

AspectBayesian PhdData Scientist
Required CredentialsPhD in Statistics, Mathematics, or related fieldBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentResearch-focused, academic or specialized industry rolesBusiness-focused, tech companies, or consulting firms
Industry UsageAcademic research, advanced analytics, specialized modelingData analysis, machine learning, business insights
Common Search/ComparisonYesYes

While a Bayesian PhD specializes in advanced statistical modeling and research, a Data Scientist applies data analysis and machine learning techniques in practical business contexts. Both roles require strong analytical skills, but the Bayesian PhD typically focuses on theoretical development, whereas the Data Scientist emphasizes application and implementation.

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Bayesian Phd jobs near you
Infographic showing various Bayesian Phd job openings in the United States as of August 2026, with employment types broken down into 77% Full Time, 21% Part Time, and 2% Contract. Highlights an 73% Physical, 4% Hybrid, and 23% Remote job distribution.

Staff AI Scientist

Intuit

Atlanta, GA • On-site

Full-time

Re-posted 25 days ago

Intuit rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

107th of 245 rated software companies

Job description

Intuit is looking for an innovative and hands-on Staff AI Scientist to join the Intuit AI team.
Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches.
Responsibilities
  • Practices leadership and communication skills to influence teams and to evangelize AI 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.
  • Runs A/B tests to draw conclusions on the impact of your team's work and communicates results to peers and leaders
  • 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
  • 4+ years of industry experience with AI science
  • BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent
  • 4+ 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.
  • Efficient in SQL, Hive, SparkSQL, etc.
  • Comfortable working in a Linux environment
  • 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

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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 ). 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.

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