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Bayesian Modeling Jobs in Chelsea, MI (NOW HIRING)

... models and strategic planning. Analyze market segmentation and pricing strategies to identify ... Applicants must have demonstrated experience with: 1) Bayesian methods, time series data and ...

Bayesian Modeling information

See Chelsea, MI salary details

$9

$55

$78

How much do bayesian modeling jobs pay per hour?

As of Jun 20, 2026, the average hourly pay for bayesian modeling in Chelsea, MI is $55.64, according to ZipRecruiter salary data. Most workers in this role earn between $49.90 and $64.71 per hour, depending on experience, location, and employer.

What is the difference between Bayesian Modeling vs Data Scientist?

AspectBayesian ModelingData Scientist
Required CredentialsStatistics, Mathematics, Data AnalysisStatistics, Computer Science, Data Analysis
Work EnvironmentResearch-focused, statistical modelingCross-functional, data analysis, visualization
Industry UsageResearch, academia, specialized analyticsBusiness, tech, finance, healthcare
Common Search/ComparisonYesYes

Bayesian Modeling and Data Scientists often overlap in skills like statistics and data analysis. Bayesian Modeling specializes in probabilistic models and statistical inference, while Data Scientists have broader roles including data cleaning, visualization, and machine learning. Both roles are essential in data-driven industries, but Bayesian Modeling is more focused on advanced statistical techniques.

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

To thrive as a Bayesian Modeler, you need a solid background in statistics, probability theory, and mathematical modeling, often supported by an advanced degree in statistics, mathematics, or a related field. Proficiency with programming languages such as R, Python, or Stan, and experience with statistical software and Bayesian inference tools are essential. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting results and collaborating with multidisciplinary teams. These skills ensure accurate model development, reliable data-driven insights, and clear communication of complex findings to stakeholders.

How does a Bayesian Modeling specialist typically collaborate with cross-functional teams in a workplace setting?

Bayesian Modeling specialists often work closely with data scientists, software engineers, and domain experts to integrate probabilistic models into larger analytical or production systems. They are involved in translating complex statistical concepts into actionable insights and recommendations tailored to business needs. Effective communication is key, as they must present findings to both technical and non-technical stakeholders, ensuring that model assumptions and results are clearly understood. Collaboration may also include contributing to code reviews, sharing best practices for model validation, and mentoring colleagues on Bayesian methodologies.

What is Bayesian modeling?

Bayesian modeling is a statistical approach that uses Bayes' Theorem to update the probability of a hypothesis as more data becomes available. It incorporates prior beliefs or knowledge, combines them with observed data, and produces a posterior probability distribution to guide inference and decision-making. This approach is widely used in various fields such as machine learning, data science, and scientific research for tasks like parameter estimation, prediction, and model selection.
What cities near Chelsea, MI are hiring for Bayesian Modeling jobs? Cities near Chelsea, MI with the most Bayesian Modeling job openings:
Department of Statistics RESEARCH FELLOW

Department of Statistics RESEARCH FELLOW

Michigan Medicine

Ann Arbor, MI • On-site

Full-time

Posted 8 days ago


Michigan Medicine rating

7.4

Company rating: 7.4 out of 10

Based on 72 frontline employees who took The Breakroom Quiz

321st of 1,001 rated hospitals


Job description

Job Summary:
Michigan Medicine is seeking a postdoctoral research fellow in statistical genetics and computational genomics within the Terhorst Lab. The role involves developing scalable methods for complex trait analysis and collaborating with researchers across various fields.
Responsibilities:
• Develop novel statistical and computational methods for ARG-based quantitative genetics
• Analyze large-scale genetic and phenotypic datasets
• Implement scalable software and algorithms for genomic inference
• Collaborate with researchers across statistics, genetics, and computational biology
• Contribute to manuscripts, presentations, and open-source software development
• Participate in interdisciplinary collaborations related to predictive breeding and genome editing
• Travel to the United Kingdom to collaborate with project partners at the University of Edinburgh and the University of Oxford
Qualifications:
Required:
• PhD in statistics, computer science, computational biology, genetics, applied mathematics, or a related quantitative field is required
• Proof of degree completion must be in hand on or before the start date
• Strong programming and computational skills
• Experience with statistical modeling, machine learning, or large-scale data analysis
Preferred:
• Experience with population genetics or statistical genetics
• Familiarity with Bayesian methods, probabilistic modeling, or graphical models
• Experience with scientific computing in Python, JAX, Torch, Julia, C++, or related languages
• Experience with high-performance computing or scalable algorithms
• Interest in interdisciplinary research spanning genomics and evolutionary biology
Company:
Michigan Medicine is a health care system and academic medical center that provides medical education and more. It is a sub-organization of University of Michigan. Founded in 1869, the company is headquartered in Ann Arbor, USA, with a team of 10001+ employees. The company is currently Late Stage.

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