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Learning Scientist information
See salary details
$37.5K - $52K
2% of jobs
$52K - $66.4K
3% of jobs
$66.4K - $80.9K
6% of jobs
$80.9K - $95.3K
9% of jobs
$100K is the 25th percentile. Wages below this are outliers.
$95.3K - $109.8K
15% of jobs
The median wage is $119.4K / yr.
$109.8K - $124.2K
22% of jobs
$132.2K is the 75th percentile. Wages above this are outliers.
$124.2K - $138.7K
32% of jobs
$138.7K - $153.1K
3% of jobs
$153.1K - $167.6K
4% of jobs
$167.6K - $182K
1% of jobs
$182K - $196.5K
2% of jobs
$37.5K
$122.7K
$196.5K
How much do learning scientist jobs pay per year?
As of Aug 29, 2026, the average yearly pay for learning scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.
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Full-time
Re-posted 4 days ago
Hexion rating
7.0
Based on 6 frontline employees who took The Breakroom Quiz
74th of 101 rated chemical manufacturers
Job description
Job Summary:
Hexion Inc. is a company that pushes boundaries and creates impactful solutions through science. They are seeking an Applied ML Scientist to lead optimization and active-learning campaigns, collaborating with R&D and manufacturing to enhance processes and drive innovation.
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.
Qualifications:
Required:
• 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:
• 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.
Company:
Based in Columbus, Ohio, Hexion Inc. is a leading global producer of adhesives and performance materials. Founded in 1989, the company is headquartered in Mount Maunganui, NZL, with a team of 1001-5000 employees. The company is currently Late Stage.
Hexion Inc. is a company that pushes boundaries and creates impactful solutions through science. They are seeking an Applied ML Scientist to lead optimization and active-learning campaigns, collaborating with R&D and manufacturing to enhance processes and drive innovation.
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.
Qualifications:
Required:
• 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:
• 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.
Company:
Based in Columbus, Ohio, Hexion Inc. is a leading global producer of adhesives and performance materials. Founded in 1989, the company is headquartered in Mount Maunganui, NZL, with a team of 1001-5000 employees. The company is currently Late Stage.
About Hexion
Sourced by ZipRecruiter
Industry
Chemical manufacturing
Company size
10,000+ Employees
Headquarters location
Columbus, OH, US
Year founded
1899