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Bayesian Modeling Jobs in Maryland (NOW HIRING)

AI Engineer

MD · On-site

$80K - $160K/yr

Develop algorithms such as Bayesian, coordinate descent, gradient descent, and evolutionary * Utilizes big data computation and storage models to create prototypes and data sets * Develop ...

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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.

NIST PREP Postdoc Associate in Statistical Analysis and Tool Development for the NIST GenAI Evaluati

Southeastern Universities Research Association

Gaithersburg, MD • On-site

$100K/yr

Full-time

Re-posted 23 hours ago


Job description

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title: Statistical Analysis and Tool Development for the NIST GenAI Evaluation Program
The work will entail: This position involves statistical analysis and analysis tool development supporting the NIST GenAI evaluation series (https://ai-challenges.nist.gov/genai) within NIST's Information Technology Laboratory. The primary project is a Testing and Evaluation (T&E) framework for generative AI watermarking, where the associate will lead the statistical analysis of evaluation results and develop the statistical engine behind NIST-CARAT (Calibrated Risk Assessment Tool for authentication technologies), an interactive tool enabling policymakers to explore the empirical performance consequences of compliance-threshold choices. This work centers on characterizing the tradeoff between content quality and watermark resilience under routine image handling, using rigorous detection-performance analysis, calibration assessment, uncertainty quantification, and Bayes-risk estimation to produce internationally defensible threshold claims.
Beyond this project, the associate will contribute statistical and analytical support across the wider NIST GenAI evaluation portfolio, which spans the evaluation of generative AI technologies across multiple modalities (text, voice, image, video, and code). This includes experimental design, metric development, analysis of evaluation outputs, and the development of reproducible analysis pipelines and interactive reporting tools. The associate will actively participate in NIST measurement science and contribute to cutting-edge research and evaluation in generative AI.
U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
  • Analyzing generative AI evaluation results using detection and classification performance methods (ROC, partial AUC, equal error rate, Brier score) and Bayes-risk characterization
  • Performing uncertainty quantification and calibration assessment to support defensible threshold and compliance claims
  • Developing the statistical backend of NIST-CARAT, including the models mapping compliance-threshold choices to expected operational consequences
  • Designing and implementing reproducible analysis pipelines from raw evaluation outputs through to estimates with calibrated uncertainty
  • Building interactive analysis and reporting tools (e.g., dashboards, interactive plots) to communicate evaluation results to technical and policy audiences
  • Processing large evaluation datasets, including work in GPU-accelerated, high-performance computing environments
  • Exploring data through descriptive statistics and graphical displays
  • Contributing to internal reports, workshop white papers, and submissions to international standards bodies (e.g., ISO/IEC JTC 1/SC 42 and SC 29)
  • Explaining statistical and metrological concepts to non-statisticians, including policy and standards stakeholders

Qualifications
  • A Ph.D. in statistics, biostatistics or a closely related quantitative field
  • Mastery of statistical analysis methods, including experimental design, detection/classification evaluation (ROC, partial AUC, EER, Brier score), calibration, uncertainty quantification, and Bayes-risk characterization
  • Experience with Bayesian modeling and Monte Carlo / Markov Chain Monte Carlo methods
  • Proficiency in a statistical computing and scripting language (e.g., R, Python) and in shell scripting, with version-controlled, reproducible analysis workflows
  • Experience building interactive analysis tools and dashboards (e.g., R Shiny, interactive plotting libraries, Jupyter notebooks)
  • Familiarity with generative AI tools, including large language models, and with AI test and evaluation
  • Experience with high-performance or GPU-accelerated computing environments, and with containerization and workflow tooling (e.g., Docker, Argo Workflows) in a data-analysis context
  • Strong communication skills in speaking, writing, and graphical display, including the ability to explain statistical concepts to non-statisticians

Privacy Act StatementAuthority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.
SURA is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
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