Senior Data Scientist

Date: May 4, 2026

Location:

Caesarea, HA, IL

Company Overview

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

We are looking for a Senior Data Scientist to lead the development of machine learning solutions powering real-time decision-making and recommendation systems in industrial production environments.

This is a hands-on role with end-to-end ownership. You will be responsible for the full modeling lifecycle, from problem framing and data exploration to modeling, evaluation, deployment, and production impact. You will work with complex, noisy sensor and control data, building models that must be robust, interpretable, and reliable in real-world conditions.

You will collaborate closely with deep learning researchers, research engineers, and domain experts, and play a key role in shaping how data science is applied across the system.

Team: AI Team

Reports to: Director of AI

Location: Hybrid – Caesarea, Israel

Type: Full-time

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