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Summer Bayesian Jobs (NOW HIRING)

Costello Medical

Statistician Intern

Boston, MA · On-site +1

$54K - $66K/yr

We are currently recruiting for start dates in Summer 2026 and you will be asked to state your ... analysis and Bayesian network meta-analysis using both standard and emerging methods. This ...

Costello Medical

Statistician Intern

Boston, MA · On-site

$54K - $66K/yr

We are currently recruiting for start dates in Summer 2026 and you will be asked to state your ... analysis and Bayesian network meta-analysis using both standard and emerging methods. This ...

Costello Medical

Statistician Intern

Boston, MA · On-site

$54K - $66K/yr

We are currently recruiting for start dates in Summer 2026 and you will be asked to state your ... analysis and Bayesian network meta-analysis using both standard and emerging methods. This ...

All JobsSummer Bayesian Jobs

Summer Bayesian information

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How much do summer bayesian jobs pay per hour?

As of Jun 29, 2026, the average hourly pay for summer bayesian in the United States is $15.89, according to ZipRecruiter salary data. Most workers in this role earn between $13.46 and $17.55 per hour, depending on experience, location, and employer.

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

To thrive as a Bayesian Statistician, you need strong mathematical and statistical knowledge, especially in probability theory, Bayesian inference, and typically a degree in statistics, mathematics, or a related field. Proficiency in statistical programming languages such as R, Python (with libraries like PyMC or Stan), and familiarity with MCMC methods are essential. Critical thinking, problem-solving, and the ability to communicate complex ideas clearly are standout soft skills in this role. These skills ensure accurate data analysis, effective modeling, and clear communication of results for informed decision-making.

What are Summer Bayesians?

A Summer Bayesian is typically a student or researcher who participates in summer programs, workshops, or internships focused on Bayesian statistics and data analysis. These programs often provide intensive training in Bayesian methods, including hands-on projects and collaboration with experts in the field. The goal is to help participants build practical skills in applying Bayesian inference to real-world problems, often in academic or industry settings. Summer Bayesians may come from diverse backgrounds such as statistics, computer science, or engineering.

What is the difference between Summer Bayesian vs Summer Data Analyst?

AspectSummer Bayesian
Required CredentialsTypically requires a background in statistics, mathematics, or data science; familiarity with Bayesian methods and programming languages like R or Python
Work EnvironmentResearch-focused, often in academic or tech companies, involving statistical modeling and data analysis
Employer & Industry UsageUsed in industries like finance, healthcare, and tech for probabilistic modeling and decision-making
Common Search & ComparisonCompared with data analysts for skill overlap and project scope

Summer Bayesian roles focus on advanced statistical modeling using Bayesian methods, requiring strong quantitative skills and programming knowledge. In contrast, Summer Data Analysts typically handle broader data processing and reporting tasks. While both roles involve data work, Summer Bayesian positions emphasize probabilistic modeling and statistical inference, making them more specialized.

What are some common challenges faced by Bayesian statisticians during summer research projects?

Summer research projects for Bayesian statisticians often involve tight timelines and rapidly evolving datasets, which can make it challenging to develop, test, and validate complex models efficiently. Collaborating with interdisciplinary teams—such as domain experts, data engineers, and fellow statisticians—requires clear communication to ensure assumptions and results are well understood by all stakeholders. Additionally, balancing exploratory analysis with rigorous statistical inference is essential to deliver actionable insights within the limited timeframe of a summer program.
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What cities are hiring for Summer Bayesian jobs? Cities with the most Summer Bayesian job openings:
What are the most commonly searched types of Bayesian jobs? The most popular types of Bayesian jobs are:
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Summer Bayesian jobs near you
Infographic showing various Summer Bayesian job openings in the United States as of June 2026, with employment types broken down into 17% Internship, 8% As Needed, 8% Full Time, 50% Temporary, and 17% Summer. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution, with an average salary of $33,041 per year, or $15.9 per hour.

AI Residency Program, Material Science (2026 Cohort)

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Posted 3 days ago

Job description

AI Resident - 2026 Cohort

The AI Residency Program is a full-time research opportunity designed to bridge the gap between academic research and industry applications in AI for materials science. Residents will work closely with Lila scientists and engineers on high-impact, open-science projects, with the option to focus on either fundamental or applied research.

  • Duration: 6-12 months (extension possible)
  • Start Dates: First hires beginning January 2026, with rolling applications and additional intakes in Summer and Fall 2026
  • Cohort Size: Small group of selected residents
  • Mentorship: Pairing with technical mentors, feedback from cross-functional teams
  • Resources: Access to proprietary datasets, high-performance compute, and Lila's research infrastructure

Research areas include ML-accelerated simulations, Bayesian methods, representation learning, generative models, agentic science, and ML-driven automation.

 
Application Requirement:
Please submit your resume alongside a research proposal (up to 3 pages, unlimited references) outlining the project you would plan to pursue during your residency at Lila Sciences. Please submit your research proposal as your cover letter. Applications without both documents will not be considered. Optional supporting materials (e.g., recommendation letters, publications, research artifacts) may also be included. 

Your Impact at Lila

The Lila Sciences AI Residency is a full-time research program at the intersection of artificial intelligence and materials science. As a resident, you'll join a cohort of researchers tackling open-ended scientific challenges alongside Lila's world-class team of scientists and engineers. With access to proprietary datasets, high-performance compute infrastructure, and experienced mentors, you'll pursue ambitious research projects with both academic and real-world impact. Publishing is encouraged but not required - what matters most is pushing the frontier of scientific discovery.

What You'll Be Building

  • Design and execute independent research projects in AI for materials science
  • Collaborate with Lila scientists and engineers on cutting-edge, open-science initiatives
  • Explore domains such as ML-accelerated simulations, Bayesian methods, representation learning, generative AI, agentic science, and ML-driven automation
  • Contribute to collaborative team research and co-develop novel approaches to scientific discovery
  • Share findings internally and externally; publications are welcome but not mandatory

What You'll Need to Succeed

  • Degree in Materials Science, Chemistry, Computer Science, AI/ML, Physics, Mathematics, or related field (Bachelor's, Master's, or PhD)
  • Proficiency in Python and deep learning frameworks (e.g., PyTorch)
  • Experience working with large-scale datasets or simulations
  • Familiarity with modern AI/ML architectures and training techniques
  • Strong research background, demonstrated through publications, thesis work, or open-source projects

Bonus Points For

  • Prior work on ML applications in scientific domains (e.g., materials discovery, chemistry, simulations)
  • Familiarity with Bayesian optimization, active learning, or generative models
  • Experience in reinforcement learning or agent-based approaches to scientific reasoning
  • Open-source contributions or collaborative research experience
  • Strong communication and writing skills, especially for conveying complex scientific ideas

Apply