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Bayesian Phd Jobs (NOW HIRING)

WEX, Inc.

PhD Intern - AI/ML/NLP Engineer

$35 - $43/hr

As a PhD Research Intern, you will apply your deep technical expertise to solve complex, real-world ... Solid grasp of probability and statistics concepts (e.g., distributions, Bayesian inference ...

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 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 are the key skills and qualifications needed to thrive as a Bayesian PhD, and why are they important?

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 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.

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.
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Bayesian Phd jobs near you

Bayesian Statistics Expert - PhD

Mercor

San Francisco, CA • Remote

$70 - $100/hr

Full-time

Posted 8 days ago

Be an early applicant

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: STEM Computational Scientific Software & Evaluation Design - Computational Bayesian Statistics and Applied Mathematics
Type: Contract
Compensation: $70–$100/hour
Location: Remote
Commitment: 15–20 hours/week

Role Responsibilities

  • Design graduate-level computational problems using domain-specific scientific software libraries such as PyMC, PyStan, FEniCS, and GUDHI.
  • Develop problems that require strategic reasoning, including designing sequences of queries or experiments to uncover hidden information.
  • Calibrate tasks against state-of-the-art AI models and refine problem designs to achieve target difficulty levels.
  • Collaborate independently and asynchronously to iterate on problem designs based on calibration feedback.
  • Utilize strong Python programming skills to write problem setups, oracle functions, and solution validators.
  • Work comfortably in a Linux/terminal environment with remote compute sandboxes.

Qualifications

Must-Have

  • Graduate-level training in a relevant STEM domain (MS, PhD, or equivalent research experience).
  • Proficiency with at least one scientific software library such as PyMC, FEniCS, or GUDHI.
  • Strong Python programming skills.
  • Ability to work independently and iterate on problem designs.
  • Comfortable in a Linux/terminal environment.

Preferred

  • Experience across multiple listed domains or tools.
  • Familiarity with benchmark or evaluation design.
  • Background in scientific pedagogy or exam/problem-set design.
  • Experience with computational reproducibility and containerized environments.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.