Imagine Everything. Build the Future with Hexion.

At Hexion, we push boundaries, rethink possibilities, and create real impact. We activate science to deliver progress—developing breakthrough solutions that strengthen industries, protect communities, and drive a more sustainable future.

This is where bold thinkers, problem-solvers, and innovators come together to shape what’s next. Whether you're engineering advanced materials, transforming manufacturing technologies, or leading strategic innovation, your ideas and actions leave a lasting mark. We cultivate an inclusive culture of growth, collaboration, and accountability, ensuring every contribution propels us forward.

We don’t follow the status quo—we challenge it, disrupt it, and improve it. Every role at Hexion is part of something bigger.

We invest in innovation, sustainability, and continuous development—equipping you with the tools, training, and opportunities to excel. With an unwavering commitment to safety, partnership, belonging, and impact, we empower you to lead change and strengthen industries worldwide.

Your Future Starts Here.

If you’re ready to push limits, reimagine what’s possible, and create the extraordinary, Hexion is where you belong.

Anything is possible when you imagine everything.

About the Role

Key Responsibilities

• Design, develop, and evaluate machine learning models for real-time industrial applications
• Own the end-to-end modeling pipeline, including problem formulation, data preparation, feature engineering, model development, evaluation, deployment, and post-production monitoring
• Translate ambiguous production challenges into well-defined data science problems
• Lead feature engineering and data exploration on complex sensor and control datasets
• Define KPIs, evaluation frameworks, and experimentation strategies
• Ensure model robustness over time, including monitoring, drift detection, and continuous improvement
• Work closely with data and research engineers to bring models into production
• Drive best practices in modeling, validation, and reproducibility
• Communicate insights, trade-offs, and recommendations clearly to stakeholders

Minimum Qualifications

• B.Sc./M.Sc. in Computer Science, Statistics, Applied Mathematics, or related field
• 5+ years of hands-on experience applying machine learning in production environments
• Strong Python skills and experience with ML libraries such as scikit-learn, XGBoost, LightGBM, or CatBoost
• Proven experience with feature engineering, model selection, and evaluation in real-world scenarios
• Strong statistical understanding and ability to reason under uncertainty
• Experience working with messy, high-dimensional, or time-series data
• Ability to independently own problems and drive them to production

Preferred Qualifications

• Experience with industrial systems or sensor-based data
• Familiarity with anomaly detection and root cause analysis
• Experience deploying and monitoring models in production (MLOps mindset)
• Exposure to deep learning approaches for time-series or complex data
• Experience working with AWS SageMaker for model development, training, or deployment
• Comfortable working in dynamic environments with evolving requirements