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Bayesian Statistics Engineer Jobs (NOW HIRING)

Explainable AI Engineer

Palo Alto, CA · Remote

$122K - $165K/yr

Bayesian statistics * Monte Carlo analysis * Mathematical optimization * Decision and Control ... Graduate Degree in Engineering or a related field * U.S. Citizenship . Pluses * Supply chain and ...

Experience with statistical programming, preferably in R and/or Python * Strong written and oral ... Bayesian statistics * Causal inference * External controls or real-world evidence * Target trial ...

Data Engineer

Chantilly, VA · On-site

$100K - $124K/yr

Data Engineer KBR is seeking a Data Engineer to support one of our government customers in Reston ... Advanced Statistical knowledge and analysis methods (e.g., Bayesian Statistics, multivariate ...

Data Engineer KBR is seeking a Data Engineer to support one of our government customers in Reston ... Advanced Statistical knowledge and analysis methods (e.g., Bayesian Statistics, multivariate ...

Strong proficiency in Python, R, or similar programming languages * Experience applying machine learning, Bayesian modeling, or advanced statistical methods to biological datasets * Proven ability to ...

$100K - $124K/yr

Data Engineer KBR is seeking a Data Engineer to support one of our government customers in Reston ... Advanced Statistical knowledge and analysis methods (e.g., Bayesian Statistics, multivariate ...

Data Engineer KBR is seeking a Data Engineer to support one of our government customers in Reston ... Advanced Statistical knowledge and analysis methods (e.g., Bayesian Statistics, multivariate ...

Showing results 21-40

Bayesian Statistics Engineer information

What does a Bayesian statistics engineer do?

A Bayesian Statistics Engineer applies Bayesian statistical methods to analyze data, build predictive models, and solve complex problems across various fields such as finance, healthcare, and technology. They focus on incorporating prior knowledge and updating probabilities as new data becomes available. Their work often involves designing experiments, implementing probabilistic models using software tools, and communicating findings to stakeholders. Additionally, they collaborate with data scientists and engineers to develop scalable statistical solutions.

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

To thrive as a Bayesian Statistics Engineer, you need a strong background in probability theory, statistical modeling, and Bayesian inference, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as Python or R, along with experience using probabilistic programming tools like Stan or PyMC, is typically required. Critical thinking, problem-solving abilities, and effective communication skills help in translating complex statistical concepts to non-experts and collaborating across teams. These competencies are essential for developing accurate predictive models and ensuring data-driven decisions in complex real-world applications.

What are some common challenges faced by Bayesian statistics engineers when integrating Bayesian models into production systems?

Bayesian Statistics Engineers often encounter challenges related to computational efficiency and scalability when deploying probabilistic models in real-world applications. Ensuring that models run efficiently with large-scale or streaming data can require advanced sampling techniques or approximate inference methods. Additionally, translating complex statistical outputs into actionable insights for stakeholders and collaborating with software engineers to maintain robust, reproducible pipelines are key aspects of the role. Effective communication and a strong understanding of both statistical theory and software engineering best practices are essential for success.

What is the difference between Bayesian Statistics Engineer vs Data Scientist?

AspectBayesian Statistics EngineerData Scientist
Required CredentialsStatistics, Data Science, or related degrees; knowledge of Bayesian methodsStatistics, Data Science, Computer Science degrees; broad skill set including machine learning
Work EnvironmentResearch-focused, analytical teams, often in tech or financeCross-functional teams, product-focused, in various industries
Employer & Industry UsageTech companies, finance, healthcare with emphasis on probabilistic modelingWide range of industries including tech, marketing, healthcare, finance
Common Search & ComparisonSpecialized in Bayesian methods, probabilistic modelingBroader data analysis, machine learning, and visualization skills

While Bayesian Statistics Engineers focus on probabilistic modeling using Bayesian methods, Data Scientists have a broader scope including machine learning, data analysis, and visualization. Both roles require strong statistical knowledge, but Bayesian Statistics Engineers specialize in Bayesian techniques for complex modeling tasks.

What are popular job titles related to Bayesian Statistics Engineer jobs?

For Bayesian Statistics Engineer jobs, the most frequently searched job titles are:

Infographic showing various Bayesian Statistics Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 89% Full Time, 7% Part Time, and 3% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Postdoctoral Scholar - Public Health Sciences

Hershey, PA • On-site

Penn State University
Colleges, Universities, and Professional Schools • 51 - 200 employees

Full-time

Re-posted 7 days ago


Penn State University rating

7.8

Company rating: 7.8 out of 10

Based on 104 frontline employees who took The Breakroom Quiz


Job description

APPLICATION INSTRUCTIONS:
  • CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.
  • CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
  • If you are NOT a current employee or student, please click "Apply" and complete the application process for external applicants.

Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.
This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.
POSITION SPECIFICS
Postdoctoral Scholar in Biostatistics: Novel Adaptive Clinical Trial Design Methodology
The Department of Public Health Sciences, Division of Biostatistics and Bioinformatics, at Penn State College of Medicine in Hershey, PA invites applications for a Postdoctoral Scholar position in biostatistics, statistics, or a closely related quantitative field. The position will focus on the development of next-generation statistical methods and software for adaptive clinical trial designs, with an emphasis on early-phase oncology trials.
The Postdoctoral Scholar will work under the mentorship of Dr. Shouhao Zhou at Penn State University, in collaboration with Dr. J. Jack Lee at MD Anderson Cancer Center and other clinical and methodological collaborators. The position provides an excellent opportunity for a highly motivated researcher to develop a strong independent research profile at the interface of Bayesian statistics, clinical trial design, optimization, computational statistics, and/or translational cancer research.
The successful candidate will contribute to methodological innovation, computational implementation, simulation studies, software development, manuscript preparation, and grant-related research activities. The work will support ongoing and planned projects on Bayesian adaptive designs, globally optimal dose-finding and phase I/II designs, pediatric and adult oncology trials, and efficient optimization algorithms for complex trial design spaces.
Required qualifications:
A PhD or equivalent doctoral degree in biostatistics, statistics, applied mathematics, operations research, computer science, engineering, or a closely related quantitative field.
Strong programming skills in at least one major scientific computing language such as R, Python, C++, or a similar language.
Ability to conduct independent research, communicate clearly, and work effectively in a multidisciplinary research environment.
Strong written communication skills and interest in publishing methodological and collaborative research.
Preferred qualifications:
Strong methodological training in statistical inference, computational statistics, reinforcement learning, or optimization.
Experience with Bayesian statistics, adaptive clinical trial design, dose-finding methods, phase I/II trial designs, model-assisted or model-based trial designs, or oncology clinical trials.
Prior publications or preprints in biostatistics, statistics, clinical trials, machine learning, optimization, or related methodological areas.
Learning and applying deep learning, agentic AI, and other AI methods in assisting research is encouraged.
Research environment and career development:
The Postdoctoral Scholar will join an active and growing biostatistics research environment at Penn State University College of Medicine, with opportunities to collaborate across Penn State, MD Anderson Cancer Center, and multidisciplinary cancer research teams. The position is designed to support the candidate's development as an independent methodological researcher through high-impact publications, software dissemination, conference presentations, grant involvement, and collaborative clinical trial design experience.
Application materials:
Applicants should submit a curriculum vitae, a brief statement of research interests, contact information for three references, and representative publications or writing samples if available. Review of applications will begin immediately and continue until the position is filled.
BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.
BENEFITS
Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being.
For more detailed information, please visit our Benefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.)
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here.
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.
Penn State is committed to and accountable for advancing equity, respect, and belonging. We embrace individual uniqueness, as well as a culture of belonging that supports equity initiatives, leverages the educational and institutional benefits of inclusion in society, and provides opportunities for engagement intended to help all members of the community thrive. We value belonging as a core strength and an essential element of the university's teaching, research, and service mission.
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