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Bayesian Jobs in Ohio (NOW HIRING)

Design and coordinate sequential experiment campaigns using Bayesian optimization and active learning, accounting for operational variabilities and constraints. * Select and maintain surrogate models ...

$80 - $100/hr

You will run and measure SEO experiments using A/B tests and Bayesian frameworks to quantify the impact on organic traffic. Additionally, you will deliver self-serve analytics through Power BI ...

Familiarity with active learning, Bayesian optimization, and related techniques. * Knowledge of ML engineering core tasks and techniques, such as data and optimization pipelines, model deployment ...

Deep knowledge of mathematical statistics, maximum likelihood estimation, sufficient statistics, hypothesis testing theory, Bayesian inference, regression analysis, multivariate methods, experimental ...

Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

Machine learning tasks such as natural language processing, image recognition, semantic segmentation, reinforcement learning, approaches such as Bayesian, deep convolutional and graph neural network ...

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Bayesian information

What is a Bayesian?

A Bayesian job typically involves applying Bayesian statistics, probabilistic modeling, and inference techniques to analyze data and make decisions under uncertainty. Professionals in this field use Bayes' theorem to update beliefs based on new evidence, often working in areas like machine learning, finance, healthcare, and research. Common roles include Bayesian statisticians, data scientists, and researchers who build probabilistic models to improve predictions and decision-making.

What are the typical projects or challenges faced in a Bayesian role?

In a Bayesian role, you’ll often work on projects involving probabilistic modeling, uncertainty quantification, and predictive analytics for real-world decision-making. Common challenges include structuring prior distributions, ensuring computational efficiency for complex models, and clearly explaining Bayesian results to non-technical stakeholders. You might collaborate closely with data engineers, domain experts, and business analysts to refine models and translate findings into actionable recommendations. This role offers the opportunity to tackle diverse analytical problems across industries like healthcare, finance, or tech, supporting ongoing professional growth and learning.

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

To thrive as a Bayesian (typically a Bayesian Data Scientist or Statistician), you need a strong background in probability theory, statistical modeling, and mathematics, often with an advanced degree in statistics, data science, or a related quantitative field. Experience with programming languages such as Python or R, Bayesian analysis libraries (e.g., Stan, PyMC), and familiarity with statistical software are commonly required. Analytical thinking, collaborative teamwork, and the ability to communicate complex results clearly are valuable soft skills in this role. These abilities are essential for designing robust models, interpreting data accurately, and delivering actionable insights to interdisciplinary teams.

What are the most commonly searched types of Bayesian jobs in Ohio?

The most popular types of Bayesian jobs in Ohio are:

Infographic showing various Bayesian job openings in Ohio as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 57% Physical, 4% Hybrid, and 39% Remote job distribution.

Applied ML Scientist - Active Learning

Hexion

Columbus, OH • On-site

$90 - $120/hr

Other

Re-posted 11 days ago


Hexion rating

7.0

Company rating: 7.0 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

73rd of 103 rated chemical manufacturers


Job description

Job Responsibilities
  • Lead the design and execution of optimization and active-learning campaigns across chemistry, formulation, and process development.
  • Collaborate with R&D and manufacturing on framing optimization problems with design spaces, decision variables, objectives, and hard and soft constraints.
  • Design and coordinate sequential experiment campaigns using Bayesian optimization and active learning, accounting for operational variabilities and constraints.
  • Select and maintain surrogate models for acquisition, using model uncertainty to drive the search and respecting each model's domain of validity.
  • Drive closed-loop optimization that connects surrogate models to experimentation, with emphasis on decision quality, exploration versus exploitation, and actionable recommendations.
  • Partner with ML engineers, software engineers, and process engineers to deploy and monitor optimization systems.
  • Explore and adopt emerging ML methods, including LLM and agentic approaches, to advance optimization.
  • Communicate methods, results, and their limitations clearly to technical and non-technical audiences.
Minimum Qualifications
  • Bachelor's degree in Computer Science, Data Science, Statistics, Operations Research, or a related field, with substantial relevant experience in ML modeling or optimization experience for chemistry, formulation, process, or manufacturing problems.
  • 7+ years experience.
  • Demonstrated expertise in Bayesian optimization and Gaussian processes, including kernels, acquisition functions, and batch, multi-objective, and constrained settings.
  • Experience designing and running experiment campaigns or closed-loop optimization.
  • Experience in applied statistics and uncertainty quantification, with emphasis on calibrated posteriors that drive acquisition.
  • Strong Python skills and experience with mainstream Python-based ML and Bayesian optimization frameworks and tools.
  • Active use of AI-assisted coding and other AI tools in daily work, with familiarity with emerging ML methods including LLM and agentic approaches.
  • Strong communication, collaboration, and stakeholder management skills for working with R&D, manufacturing, and business teams.
Preferred Qualifications
  • Master's degree in Computer Science, Data Science, Statistics, Operations Research, or a related field.
  • Experience with active learning and physics-informed approaches for optimization in chemical synthesis, formulation, or process development.
  • Experience working with manufacturing, process, quality, or plant data, including issues such as batch-to-batch variability, raw-material variability, model drift, and changing operating conditions.
  • Familiar with ML engineering core tasks and techniques, such as data and optimization pipelines, model deployment, and MLOps.
  • Knowledge of chemistry ML core areas such as cheminformatics, molecular representation, predictive modeling, and chemistry foundation models.
Equal Opportunity Statement

We are an Equal Opportunity, Affi­ndant Anti­Ass­istant employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law.

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