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Applied ML Scientist - Active Learning

Columbus, OH • On-site

Hexion
Chemical Manufacturing • 10K+ employees
Learn more about Hexionexternal

Other

Re-posted 20 days ago

Hexion rating

7.0

Company rating: 7.0 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

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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