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Bayesian Phd Jobs (NOW HIRING)

Numerator

Sr. Data Scientist II

... PhD in a quantitative field * Clear communication with both technical and non-technical audiences Nice to Haves: * Diagnosing and debugging large Bayesian models - convergence and divergence issues ...

General Atomics

Data Scientist III

Charlottesville, VA · On-site

$98K - $171K/yr

Job Summary Are you a statistician who wants to see your Bayesian models protect national security? GA-Intelligence is seeking a Statistics PhD to develop tracking algorithms that process data from ...

General Atomics

Data Scientist III

Charlottesville, VA · On-site

Job Summary Are you a statistician who wants to see your Bayesian models protect national security? GA-Intelligence is seeking a Statistics PhD to develop tracking algorithms that process data from ...

Jobs for Humanity

Postdoctoral Fellows

Bloomington, IN

$45K - $61K/yr

The ideal individual will have a strong history in coding in R, SASS, and/or Python, Bayesian ... Completed PhD in biostatistics, statistics, computer science, or a related discipline. Strong ...

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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.

POSTDOCTORAL RESEARCHER-Bayesian Biostatistics for Multicenter Clinical Trials-DeSantis Lab

University Of Texas Southwestern Medical Cent (The

Dallas, TX • On-site

Full-time

Medical, Retirement, PTO

Posted 8 days ago

UT Southwestern rating

7.9

Company rating: 7.9 out of 10

Based on 152 frontline employees who took The Breakroom Quiz

107th of 889 rated healthcare providers

Job description

POSTDOCTORAL RESEARCHER

UTSW invites applications for a Postdoctoral Fellow in Bayesian Biostatistics. The fellow will contribute to both applied and methodological research involving Bayesian methods for multicenter clinical trials, with a particular focus on phase III studies in trauma, trauma resuscitation, and traumatic brain injury.

This position is suited to a recent PhD graduate who is interested in developing a research program at the intersection of Bayesian methodology and collaborative clinical-trial research. The fellow will work closely with biostatisticians, clinical investigators, trial leadership, and multidisciplinary research teams involved in large, multicenter studies.

Research activities

The fellow's work will include:

  • Conducting statistical analyses of multicenter clinical-trial data.

  • Developing statistical analysis plans, including specification of estimands, prior distributions, analysis populations, missing-data methods, interim analyses, and sensitivity analyses.

  • Contributing to the design, conduct, monitoring, and reporting of phase III clinical trials.

  • Developing and evaluating Bayesian methods relevant to multicenter trials.

  • Conducting simulation studies to assess type I error, power, bias, interval coverage, operating characteristics, and robustness to prior-data conflict.

  • Preparing manuscripts, abstracts, presentations, technical reports, and grant applications.

  • Collaborating with clinicians and investigators in trauma, trauma resuscitation, and traumatic brain injury research.

Methodological projects may include, but are not limited to:

  • Bayesian borrowing of historical or external information.

  • Dynamic and robust borrowing methods.

  • Hierarchical models for multicenter studies.

  • Commensurate, mixture, power, and meta-analytic predictive priors.

  • Bayesian approaches to phase III confirmatory trials.

  • Bayesian interim monitoring and adaptive trial methods.

  • Modeling treatment-effect heterogeneity across centers or patient subgroups.

  • Methods for ordinal, longitudinal, survival, and competing-risk outcomes.

  • Sensitivity analyses for prior specification, missing data, and departures from modeling assumptions.

The fellow will have opportunities to lead methodological and applied manuscripts, present findings at national scientific meetings, and participate in the development of grant proposals and future clinical-trial protocols.

Appointment and mentorship

The fellow will receive mentorship in:

  • Bayesian methodology development.

  • Statistical leadership in multicenter clinical trials.

  • Collaborative biostatistics.

  • Manuscript and grant preparation.

  • Development of an independent research program.

The initial appointment will be for one year.

Required qualifications

Applicants should have:

  • A PhD in biostatistics, statistics, or a closely related quantitative field, completed recently-typically within the past one to two years-or expected before the position start date.

  • Formal training in Bayesian statistical methods.

  • Strong programming skills in R.

  • Experience with statistical simulation and reproducible data analysis.

  • A strong foundation in statistical inference, regression modeling, and clinical-trial methodology.

  • The ability to communicate statistical concepts effectively to both quantitative and clinical collaborators.

  • Strong scientific writing and organizational skills.

  • An interest in conducting both applied and methodological research.

Preferred qualifications

Preferred experience includes one or more of the following:

  • Bayesian hierarchical modeling or Bayesian borrowing methods.

  • Clinical-trial design or analysis.

  • Development of statistical analysis plans.

  • Multicenter or cluster-structured data.

  • Stan, RStan, cmdstanr, JAGS, NIMBLE, or other Bayesian computing platforms.

  • Longitudinal, ordinal, time-to-event, or competing-risk methods.

  • Analysis of trauma, emergency medicine, critical care, or neurological outcomes.

  • Preparation of peer-reviewed manuscripts or collaborative grant proposals.

Prior experience in trauma research is not required. Applicants with strong Bayesian training and an interest in clinical-trial applications are encouraged to apply.

Interested individuals should send a CV and a list of three references to:

Stacia DeSantis


Stacia.Desantis@UTSouthwestern.edu

UTSouthwestern Medical Center is committed to an educational and working environment that provides equal opportunity to all members of the University community. Asan equal opportunity employer, UT Southwestern prohibits unlawful discrimination, including discrimination on the basis ofrace, color, religion, national origin, sex,sexual orientation, gender identity, gender expression,age, disability, genetic information, citizenship status, or veteran status.

This position is security-sensitive and subject to Texas Education Code 51.215, which authorizes UT Southwestern to obtain criminal history record information.

Appointment rank will be commensurate with academic accomplishment and experience. Consideration may be given to applicants seeking less than a full-time schedule.

To learn more about the benefits UT Southwestern offers, visithttps://www.utsouthwestern.edu/employees/hr-resources/

Benefits
    UT Southwestern is proud to offer a competitive and comprehensive benefits package to eligible employees. Our benefits are designed to support your overall wellbeing, and include:
    • PPO medical plan, available day one at no cost for full-time employee-only coverage
    • 100% coverage for preventive healthcare - no copay
    • Paid Time Off, available day one
    • Retirement Programs through the Teacher Retirement System of Texas (TRS)
    • Paid Parental Leave Benefit
    • Wellness programs
    • Tuition Reimbursement
    • Public Service Loan Forgiveness (PSLF) Qualified Employer
    • Learn more about these and other UTSW employee benefits!

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