Data Scientist Engineer

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