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Bayesian Modeling Jobs in Illinois (NOW HIRING)

Non-Linear Regression Analysis, Multivariate Analysis, Bayesian Methods, Generalized Linear Models, Decision Trees and Random Forest, Non Parametric estimations, Neural Networks, or Ensemble Models;

$160K - $200K/yr

Own commercial auto pricing models end-to-end from feature development through deployment and ... GLMs, GBDTs, time series analysis, heavy tail distributions, and Bayesian methods * Proficiency in ...

Apply knowledge and training in data modeling, data mining and optimization to very large scale ... Bayesian Data Analysis, Classification, Clustering, Regression, Collaborative Filtering, and Graph ...

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Bayesian Modeling information

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 in Illinois are hiring for Bayesian Modeling jobs? Cities in Illinois with the most Bayesian Modeling job openings:
Assistant Professors, Biomedical Informatics #PED230

Assistant Professors, Biomedical Informatics #PED230

The University of Chicago

Chicago, IL • On-site

Full-time

Medical, Retirement, PTO

Posted 26 days ago


University Of Chicago rating

8.2

Company rating: 8.2 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

111th of 541 rated colleges and universities


Job description

Description
The University of Chicago's Department of Pediatrics, Section of Biomedical Informatics, is seeking full-time faculty members at the rank of assistant professor to advance the department's clinical quality improvement, operational analytics, and clinical research informatics programs. Appointees will collaborate with clinicians, quality leaders, researchers, and operational teams to develop reproducible analytic pipelines; design patient cohorts, registries, and quality metrics; and translate EHR, laboratory, and operational data into actionable insights that improve clinical care and support the department's strategic goals.
Compensation is dependent upon qualifications. These positions are benefits eligible. The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.
Faculty will play a key role in developing and optimizing clinical quality metrics. They will also construct and maintain sustainable informatics infrastructure, as well as design integrated reporting solutions that leverage Epic's ecosystem (e.g., Reporting Workbench, Registries, Radar dashboards, and Clarity reporting). Responsibilities will include creating and maintaining data pipelines for internal quality monitoring and external reporting requirements (such as eCQMs, accreditation metrics, and U.S. News & World Report submissions); analyzing workflows, resource utilization, throughput, and follow-up adherence; and integrating diverse clinical data sources to support decision-making at both the point-of-care and leadership levels. Additional responsibilities include teaching and supervising students, trainees, and fellows; providing methodological support for departmental research projects; and contributing to the design and evaluation of informatics tools used across the clinical enterprise.
Faculty will be expected to develop and sustain a scholarly research program aligned with the missions of the Section of Biomedical Informatics and the Department of Pediatrics. Areas of focus may include biomedical informatics, clinical data modeling, interoperability standards, and methods for transforming EHR and operational data into actionable knowledge. Appointees may pursue methodological work in semantic data modeling, HL7/FHIR standard development, clinical terminology harmonization, or scalable approaches to data interoperability, as well as applied research stemming from clinical quality initiatives, operational analytics, or data-driven workflow improvement.
Opportunities exist to collaborate with institutional partners including the Biological Sciences Division, the Center for Research Informatics (CRI), the Health Data Science Institute (HDSI), the Center for Personalized Therapeutics, the Pediatric Cancer Data Commons (PCDC), the Section of Genomics and Data Science within Pediatrics, and other University research initiatives such as the Institute for Translational Medicine (ITM) and the Institute for Population and Precision Health (IPPH).
Prior to the start of employment, qualified applicants must: 1) a doctoral degree (or equivalent) in biostatistics, statistics, biomedical informatics, computer science, applied mathematics, data science, or a related field by the start of appointment and, 2) Completed postdoctoral training prior to the start of employment.
We welcome applicants with experience in informatics, clinical quality analytics, advanced data modeling, EHR data extraction, or applied biostatistics. Candidates with experience developing informatics tools, EHR integrations, FHIR or interoperability solutions, and clinical-decision support workflows are strongly encouraged to apply. Preference will be given to candidates with demonstrated expertise in one or more of the following areas: analysis of large clinical datasets; development of clinical quality or operational metrics; building Epic-based clinical analytics solutions; interoperability standards (e.g., HL7, FHIR); biomarker or phenotype modeling; Bayesian or predictive modeling; or the analysis of genomics or other omics-scale data. Experience supporting clinical workflows and quality improvement initiatives are desirable.
To be considered, applicants must apply through The University of Chicago's Academic Recruitment job board, which uses Interfolio to accept applications: https://apply.interfolio.com/183666. Applicants must upload: a CV including bibliography and a cover letter. Review of complete applications ends when the positions are filled.
For instructions on the Interfolio application process, please visit http://tiny.cc/InterfolioHelp.

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