Applied ML Scientist - Active Learning

Hexion; Columbus (OH); On-site; USD 90,000 - 120,000; Full time

Job summary

Hexion, located in Columbus, Ohio, is seeking an experienced professional to lead the design and execution of optimization and active-learning campaigns. Your role will involve collaboration with R&D and manufacturing, with a strong focus on Bayesian optimization and machine learning methods.

Candidates should possess a Bachelor's degree in a relevant field, have over 7 years of experience in optimization, and demonstrate expertise in statistical methods and Python. This position offers an opportunity to work in a dynamic environment covering various optimization challenges.

Qualifications

• Minimum 7 years of ML modeling or optimization experience.
• Strong expertise in Bayesian optimization and Gaussian processes.
• Experience in applied statistics and uncertainty quantification.

Responsibilities

• Lead optimization and active-learning campaigns in chemistry and formulation.
• Collaborate with R&D and manufacturing on optimization problems.
• Design experiment campaigns using Bayesian optimization.

Skills

Machine Learning

Bayesian Optimization

Python

Statistics

Collaboration Skills

Education

Bachelor's degree in Computer Science, Data Science, Statistics, Operations Research or related field

Master's degree preferred

Tools

Python-based ML frameworks

AI-assisted coding tools

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.